Studies

pyEX.studies.technicals.cycle.ht_dcperiod(client, symbol, timeframe='6m', col='close')

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

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (string) –
• col (string) –

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.cycle.ht_dcphase(client, symbol, timeframe='6m', col='close')

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

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (string) –
• col (string) –

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.cycle.ht_phasor(client, symbol, timeframe='6m', col='close')

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

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (string) –
• col (string) –

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.cycle.ht_sine(client, symbol, timeframe='6m', col='close')

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

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (string) –
• col (string) –

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.cycle.ht_trendmode(client, symbol, timeframe='6m', col='close')

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

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (string) –
• col (string) –

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.math.acos(client, symbol, timeframe='6m', col='close')

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

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (string) –
• col (string) –

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.math.add(client, symbol, timeframe='6m', col1='open', col2='close')

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

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (string) –
• col1 (string) –
• col2 (string) –

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.math.asin(client, symbol, timeframe='6m', col='close')

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

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (string) –
• col (string) –

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.math.atan(client, symbol, timeframe='6m', col='close')

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

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (string) –
• col (string) –

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.math.ceil(client, symbol, timeframe='6m', col='close')

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

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (string) –
• col (string) –

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.math.cos(client, symbol, timeframe='6m', col='close')

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

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (string) –
• col (string) –

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.math.cosh(client, symbol, timeframe='6m', col='close')

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

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (string) –
• col (string) –

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.math.div(client, symbol, timeframe='6m', col1='open', col2='close')

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

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (string) –
• col1 (string) –
• col2 (string) –

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.math.exp(client, symbol, timeframe='6m', col='close')

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

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (string) –
• col (string) –

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.math.floor(client, symbol, timeframe='6m', col='close')

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

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (string) –
• col (string) –

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.math.ln(client, symbol, timeframe='6m', col='close')

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

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (string) –
• col (string) –

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.math.log10(client, symbol, timeframe='6m', col='close')

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

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (string) –
• col (string) –

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.math.max(client, symbol, timeframe='6m', col='close', period=30)

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

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (string) –
• col (string) –
• period (int) –

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.math.maxindex(client, symbol, timeframe='6m', col='close', period=30)

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

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (string) –
• col (string) –
• period (int) –

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.math.min(client, symbol, timeframe='6m', col='close', period=30)

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

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (string) –
• col (string) –
• period (int) –

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.math.minindex(client, symbol, timeframe='6m', col='close', period=30)

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

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (string) –
• col (string) –
• period (int) –

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.math.minmax(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (string) –
• col (string) –
• period (int) –

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.math.minmaxindex(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (string) –
• col (string) –
• period (int) –

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.math.mult(client, symbol, timeframe='6m', col1='open', col2='close')

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

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (string) –
• col1 (string) –
• col2 (string) –

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.math.sin(client, symbol, timeframe='6m', col='close')

This will return a dataframe of Vector Trigonometric SIN for the given symbol across the given timeframe

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (string) –
• col (string) –

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.math.sinh(client, symbol, timeframe='6m', col='close')

This will return a dataframe of Vector Trigonometric Sinh for the given symbol across the given timeframe

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (string) –
• col (string) –

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.math.sqrt(client, symbol, timeframe='6m', col='close')

This will return a dataframe of Vector Square Root for the given symbol across the given timeframe

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (string) –
• col (string) –

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.math.sub(client, symbol, timeframe='6m', col1='open', col2='close')

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

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (string) –
• col1 (string) –
• col2 (string) –

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.math.sum(client, symbol, timeframe='6m', col='close', period=30)

This will return a dataframe of Summation for the given symbol across the given timeframe

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (string) –
• col (string) –
• period (int) –

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.math.tan(client, symbol, timeframe='6m', col='close')

This will return a dataframe of Vector Trigonometric Tan for the given symbol across the given timeframe

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (string) –
• col (string) –

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.math.tanh(client, symbol, timeframe='6m', col='close')

This will return a dataframe of Vector Trigonometric Tanh for the given symbol across the given timeframe

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (string) –
• col (string) –

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.momentum.adx(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.momentum.adxr(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.momentum.apo(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.momentum.aroon(client, symbol, timeframe='6m', highcol='high', lowcol='low', period=14)

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

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.momentum.aroonosc(client, symbol, timeframe='6m', highcol='high', lowcol='low', period=14)

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

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.momentum.bop(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.momentum.cci(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.momentum.cmo(client, symbol, timeframe='6m', col='close', period=14)

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

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe to use, for pyEX.chart
• col (string) – column to use to calculate
• period (int) – period to calculate across

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.momentum.dx(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.momentum.macd(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.momentum.macdext(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.momentum.mfi(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.momentum.minus_di(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.momentum.minus_dm(client, symbol, timeframe='6m', highcol='high', lowcol='low', period=14)

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

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.momentum.mom(client, symbol, timeframe='6m', col='close', period=14)

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

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe to use, for pyEX.chart
• col (string) – column to use to calculate
• period (int) – period to calculate across

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.momentum.plus_di(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.momentum.plus_dm(client, symbol, timeframe='6m', highcol='high', lowcol='low', period=14)

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

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.momentum.ppo(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.momentum.roc(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe to use, for pyEX.chart
• col (string) – column to use to calculate
• period (int) – period to calculate across

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.momentum.rocp(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe to use, for pyEX.chart
• col (string) – column to use to calculate
• period (int) – period to calculate across

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.momentum.rocr(client, symbol, timeframe='6m', col='close', period=14)

This will return a dataframe of Rate of change ratio: (price/prevPrice) for the given symbol across the given timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe to use, for pyEX.chart
• col (string) – column to use to calculate
• period (int) – period to calculate across

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.momentum.rocr100(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe to use, for pyEX.chart
• col (string) – column to use to calculate
• period (int) – period to calculate across

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.momentum.rsi(client, symbol, timeframe='6m', col='close', period=14)

This will return a dataframe of Relative Strength Index for the given symbol across the given timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe to use, for pyEX.chart
• col (string) – column to use to calculate
• period (int) – period to calculate across

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.momentum.stoch(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.momentum.stochf(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.momentum.stochrsi(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.momentum.trix(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe to use, for pyEX.chart
• col (string) – column to use to calculate
• period (int) – period to calculate across

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.momentum.ultosc(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.momentum.willr(client, symbol, timeframe='6m', highcol='high', lowcol='low', closecol='close', period=14)

This will return a dataframe of Williams’ % R for the given symbol across the given timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.overlap.bollinger(client, symbol, timeframe='6m', col='close', period=2)

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

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (string) –
• col (string) –
• period (int) –

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.overlap.dema(client, symbol, timeframe='6m', col='close', periods=None)

This will return a dataframe of double exponential moving average

for the given symbol across the given timeframe

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (string) –
• col (string) –
• periods (int) –

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.overlap.ema(client, symbol, timeframe='6m', col='close', periods=None)

This will return a dataframe of exponential moving average

for the given symbol across the given timeframe

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (string) –
• col (string) –
• periods (int) –

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.overlap.ht_trendline(client, symbol, timeframe='6m', col='close')

This will return a dataframe of hilbert trendline

for the given symbol across the given timeframe

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (string) –
• col (string) –

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.overlap.kama(client, symbol, timeframe='6m', col='close', period=30)

This will return a dataframe of kaufman adaptive moving average

for the given symbol across the given timeframe

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (string) –
• col (string) –
• period (int) –

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.overlap.mama(client, symbol, timeframe='6m', col='close', fastlimit=0, slowlimit=0)

This will return a dataframe of mesa adaptive moving average

for the given symbol across the given timeframe

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (string) –
• col (string) –
• fastlimit (int) –
• slowlimit (int) –

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.overlap.mavp(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (string) –
• col (string) –
• periods (int) –
• minperiod (int) –
• maxperiod (int) –
• matype (int) –

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.overlap.midpice(client, symbol, timeframe='6m', col='close', period=14)

This will return a dataframe of midprice over period

for the given symbol across the given timeframe

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (string) –
• col (string) –
• period (int) –

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.overlap.midpoint(client, symbol, timeframe='6m', col='close', period=14)

This will return a dataframe of midpoint over period

for the given symbol across the given timeframe

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (string) –
• col (string) –
• period (int) –

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.overlap.sar(client, symbol, timeframe='6m', highcol='high', lowcol='low', acceleration=0, maximum=0)

This will return a dataframe of parabolic sar

for the given symbol across the given timeframe

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (string) –
• highcol (string) –
• lowcol (string) –
• acceleration (int) –
• maximum (int) –

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.overlap.sarext(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (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

pyEX.studies.technicals.overlap.sma(client, symbol, timeframe='6m', col='close', periods=None)

This will return a dataframe of exponential moving average

for the given symbol across the given timeframe

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (string) –
• col (string) –
• periods (int) –

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.overlap.t3(client, symbol, timeframe='6m', col='close', periods=None, vfactor=0)

This will return a dataframe of tripple exponential moving average

for the given symbol across the given timeframe

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (string) –
• col (string) –
• periods (int) –
• vfactor (int) –

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.overlap.tema(client, symbol, timeframe='6m', col='close', periods=None)

This will return a dataframe of triple exponential moving average

for the given symbol across the given timeframe

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (string) –
• col (string) –
• periods (int) –

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.overlap.trima(client, symbol, timeframe='6m', col='close', periods=None)

This will return a dataframe of triangular moving average

for the given symbol across the given timeframe

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (string) –
• col (string) –
• periods (int) –

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.overlap.wma(client, symbol, timeframe='6m', col='close', periods=None)

This will return a dataframe of weighted moving average

for the given symbol across the given timeframe

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (string) –
• col (string) –
• periods (int) –

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.pattern.cdl2crows(client, symbol, timeframe='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

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

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdl3blackcrows(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdl3inside(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdl3linestrike(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdl3outside(client, symbol, timeframe='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

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

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdl3starsinsouth(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdl3whitesoldiers(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdlabandonedbaby(client, symbol, timeframe='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

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

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdladvanceblock(client, symbol, timeframe='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

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

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdlbelthold(client, symbol, timeframe='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

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

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdlbreakaway(client, symbol, timeframe='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

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

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdlclosingmarubozu(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdlconcealbabyswallow(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdlcounterattack(client, symbol, timeframe='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

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

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdldarkcloudcover(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdldoji(client, symbol, timeframe='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

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

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdldojistar(client, symbol, timeframe='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

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

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdldragonflydoji(client, symbol, timeframe='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

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

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdlengulfing(client, symbol, timeframe='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

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

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdleveningdojistar(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdleveningstar(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdlgapsidesidewhite(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdlgravestonedoji(client, symbol, timeframe='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

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

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdlhammer(client, symbol, timeframe='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

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

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdlhangingman(client, symbol, timeframe='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

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

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdlharami(client, symbol, timeframe='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

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

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdlharamicross(client, symbol, timeframe='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

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

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdlhighwave(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdlhikkake(client, symbol, timeframe='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

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

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdlhikkakemod(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdlhomingpigeon(client, symbol, timeframe='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

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

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdlidentical3crows(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdlinneck(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdlinvertedhammer(client, symbol, timeframe='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

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

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdlkicking(client, symbol, timeframe='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

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

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdlkickingbylength(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdlladderbottom(client, symbol, timeframe='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

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

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdllongleggeddoji(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdllongline(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdlmarubozu(client, symbol, timeframe='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

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

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdlmatchinglow(client, symbol, timeframe='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

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

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdlmathold(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdlmorningdojistar(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdlmorningstar(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdlonneck(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdlpiercing(client, symbol, timeframe='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

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

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdlrickshawman(client, symbol, timeframe='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

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

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdlrisefall3methods(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdlseparatinglines(client, symbol, timeframe='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

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

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdlshootingstar(client, symbol, timeframe='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

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

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdlshortline(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdlspinningtop(client, symbol, timeframe='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

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

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdlstalledpattern(client, symbol, timeframe='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

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

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdlsticksandwich(client, symbol, timeframe='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

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

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdltakuri(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdltasukigap(client, symbol, timeframe='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

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

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdlthrusting(client, symbol, timeframe='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

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

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdltristar(client, symbol, timeframe='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

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

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdlunique3river(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdlupsidegap2crows(client, symbol, timeframe='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of upside gap two crows for the given symbol across the given timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.pattern.cdlxsidegap3methods(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.price.avgprice(client, symbol, timeframe='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

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

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.price.medprice(client, symbol, timeframe='6m', highcol='high', lowcol='low')

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

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe to use, for pyEX.chart
• highcol (string) – column to use to calculate
• lowcol (string) – column to use to calculate

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.price.typprice(client, symbol, timeframe='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of typical price for the given symbol across the given timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.price.wclprice(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.statistic.beta(client, symbol, timeframe='6m', highcol='high', lowcol='low', period=14)

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

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.statistic.correl(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.statistic.linearreg(client, symbol, timeframe='6m', closecol='close', period=14)

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

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe to use, for pyEX.chart
• closecol (string) – column to use to calculate
• period (int) – period to calculate adx across

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.statistic.linearreg_angle(client, symbol, timeframe='6m', closecol='close', period=14)

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

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe to use, for pyEX.chart
• closecol (string) – column to use to calculate
• period (int) – period to calculate adx across

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.statistic.linearreg_intercept(client, symbol, timeframe='6m', closecol='close', period=14)

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

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe to use, for pyEX.chart
• closecol (string) – column to use to calculate
• period (int) – period to calculate adx across

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.statistic.linearreg_slope(client, symbol, timeframe='6m', closecol='close', period=14)

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

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe to use, for pyEX.chart
• closecol (string) – column to use to calculate
• period (int) – period to calculate adx across

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.statistic.stddev(client, symbol, timeframe='6m', closecol='close', period=14, nbdev=1)

This will return a dataframe of standard deviation for the given symbol across the given timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.statistic.tsf(client, symbol, timeframe='6m', closecol='close', period=14, nbdev=1)

This will return a dataframe of standard deviation for the given symbol across the given timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe to use, for pyEX.chart
• closecol (string) – column to use to calculate
• period (int) – period to calculate adx across

Returns:

result

Return type:

DataFrame

pyEX.studies.technicals.statistic.var(client, symbol, timeframe='6m', closecol='close', period=14, nbdev=1)

This will return a dataframe of var for the given symbol across the given timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.volatility.atr(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.volatility.natr(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.volatility.trange(client, symbol, timeframe='6m', highcol='high', lowcol='low', closecol='close')

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

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.volume.ad(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.volume.adosc(client, symbol, timeframe='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 timeframe

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe 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

pyEX.studies.technicals.volume.obv(client, symbol, timeframe='6m', closecol='close', volumecol='volume')

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

Parameters:

• client (pyEX.Client) – Client
• symbol (string) – Ticker
• timeframe (string) – timeframe to use, for pyEX.chart
• closecol (string) – column to use to calculate
• volumecol (string) – column to use to calculate

Returns:

result

Return type:

DataFrame

pyEX.studies.peercorrelation.peerCorrelation(client, symbol, timeframe='6m')

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

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (string) –

Returns:

result

Return type:

DataFrame

pyEX.studies.peercorrelation.peerCorrelationPlot(client, symbol, timeframe='6m')

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

Parameters:

• client (pyEX.Client) –
• symbol (string) –
• timeframe (string) –

Returns:

result

Return type:

DataFrame

pyEX.studies.utils.tolist(val)