# *****************************************************************************
#
# Copyright (c) 2020, the pyEX authors.
#
# This file is part of the pyEX library, distributed under the terms of
# the Apache License 2.0. The full license can be found in the LICENSE file.
#
import pandas as pd
import talib as t
[docs]def 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
Args:
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:
DataFrame: result
"""
df = client.chartDF(symbol, timeframe)
adx = t.ADX(
df[highcol].values.astype(float),
df[lowcol].values.astype(float),
df[closecol].values.astype(float),
period,
)
return pd.DataFrame(
{
highcol: df[highcol].values,
lowcol: df[lowcol].values,
closecol: df[closecol].values,
"adx": adx,
}
)
[docs]def 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
Args:
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:
DataFrame: result
"""
df = client.chartDF(symbol, timeframe)
adx = t.ADXR(
df[highcol].values.astype(float),
df[lowcol].values.astype(float),
df[closecol].values.astype(float),
period,
)
return pd.DataFrame(
{
highcol: df[highcol].values,
lowcol: df[lowcol].values,
closecol: df[closecol].values,
"adx": adx,
}
)
[docs]def 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
Args:
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:
DataFrame: result
"""
df = client.chartDF(symbol, timeframe)
apo = t.APO(df[col].values.astype(float), fastperiod, slowperiod, matype)
return pd.DataFrame({col: df[col].values, "apo": apo})
[docs]def 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
Args:
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:
DataFrame: result
"""
df = client.chartDF(symbol, timeframe)
aroondown, aroonup = t.AROON(
df[highcol].values.astype(float), df[lowcol].values.astype(float), period
)
return pd.DataFrame(
{
highcol: df[highcol].values,
lowcol: df[lowcol].values,
"aroonup": aroonup,
"aroondown": aroondown,
}
)
[docs]def 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
Args:
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:
DataFrame: result
"""
df = client.chartDF(symbol, timeframe)
x = t.AROONOSC(
df[highcol].values.astype(float), df[lowcol].values.astype(float), period
)
return pd.DataFrame(
{highcol: df[highcol].values, lowcol: df[lowcol].values, "aroonosc": x}
)
[docs]def 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
Args:
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:
DataFrame: result
"""
df = client.chartDF(symbol, timeframe)
x = t.BOP(
df[highcol].values.astype(float),
df[lowcol].values.astype(float),
df[closecol].values.astype(float),
df[volumecol].values.astype(float),
)
return pd.DataFrame(
{
highcol: df[highcol].values,
lowcol: df[lowcol].values,
closecol: df[closecol].values,
volumecol: df[volumecol].values,
"bop": x,
}
)
[docs]def 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
Args:
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:
DataFrame: result
"""
df = client.chartDF(symbol, timeframe)
x = t.CCI(
df[highcol].values.astype(float),
df[lowcol].values.astype(float),
df[closecol].values.astype(float),
period,
)
return pd.DataFrame(
{
highcol: df[highcol].values,
lowcol: df[lowcol].values,
closecol: df[closecol].values,
"cci": x,
}
)
[docs]def 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
Args:
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:
DataFrame: result
"""
df = client.chartDF(symbol, timeframe)
return pd.DataFrame(
{col: df[col].values, "cmo": t.CMO(df[col].values.astype(float), period)}
)
[docs]def 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
Args:
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:
DataFrame: result
"""
df = client.chartDF(symbol, timeframe)
x = t.DX(
df[highcol].values.astype(float),
df[lowcol].values.astype(float),
df[closecol].values.astype(float),
period,
)
return pd.DataFrame(
{
highcol: df[highcol].values,
lowcol: df[lowcol].values,
closecol: df[closecol].values,
"dx": x,
}
)
[docs]def 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
Args:
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:
DataFrame: result
"""
df = client.chartDF(symbol, timeframe)
macd, macdsignal, macdhist = t.MACD(
df[col].values.astype(float), fastperiod, slowperiod, signalperiod
)
return pd.DataFrame(
{
col: df[col].values,
"macd": macd,
"macdsignal": macdsignal,
"macdhist": macdhist,
}
)
[docs]def 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
Args:
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:
DataFrame: result
"""
df = client.chartDF(symbol, timeframe)
macd, macdsignal, macdhist = t.MACDEXT(
df[col].values.astype(float), fastperiod, slowperiod, signalperiod
)
return pd.DataFrame(
{
col: df[col].values,
"macd": macd,
"macdsignal": macdsignal,
"macdhist": macdhist,
}
)
[docs]def 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
Args:
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:
DataFrame: result
"""
df = client.chartDF(symbol, timeframe)
x = t.MFI(
df[highcol].values.astype(float),
df[lowcol].values.astype(float),
df[closecol].values.astype(float),
df[volumecol].values.astype(float),
period,
)
return pd.DataFrame(
{
highcol: df[highcol].values,
lowcol: df[lowcol].values,
closecol: df[closecol].values,
volumecol: df[volumecol].values,
"mfi": x,
}
)
[docs]def 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
Args:
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:
DataFrame: result
"""
df = client.chartDF(symbol, timeframe)
x = t.MINUS_DI(
df[highcol].values.astype(float),
df[lowcol].values.astype(float),
df[closecol].values.astype(float),
period,
)
return pd.DataFrame(
{
highcol: df[highcol].values,
lowcol: df[lowcol].values,
closecol: df[closecol].values,
"minus_di": x,
}
)
[docs]def 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
Args:
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:
DataFrame: result
"""
df = client.chartDF(symbol, timeframe)
x = t.MINUS_DM(
df[highcol].values.astype(float), df[lowcol].values.astype(float), period
)
return pd.DataFrame(
{highcol: df[highcol].values, lowcol: df[lowcol].values, "minus_dm": x}
)
[docs]def mom(client, symbol, timeframe="6m", col="close", period=14):
"""This will return a dataframe of
Momentum
for the given symbol across the given timeframe
Args:
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:
DataFrame: result
"""
df = client.chartDF(symbol, timeframe)
return pd.DataFrame(
{col: df[col].values, "mom": t.MOM(df[col].values.astype(float), period)}
)
[docs]def 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
Args:
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:
DataFrame: result
"""
df = client.chartDF(symbol, timeframe)
x = t.PLUS_DI(
df[highcol].values.astype(float),
df[lowcol].values.astype(float),
df[closecol].values.astype(float),
period,
)
return pd.DataFrame(
{
highcol: df[highcol].values,
lowcol: df[lowcol].values,
closecol: df[closecol].values,
"plus_di": x,
}
)
[docs]def 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
Args:
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:
DataFrame: result
"""
df = client.chartDF(symbol, timeframe)
x = t.PLUS_DM(
df[highcol].values.astype(float), df[lowcol].values.astype(float), period
)
return pd.DataFrame(
{highcol: df[highcol].values, lowcol: df[lowcol].values, "plus_dm": x}
)
[docs]def 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
Args:
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:
DataFrame: result
"""
df = client.chartDF(symbol, timeframe)
ppo = t.PPO(df[col].values.astype(float), fastperiod, slowperiod, matype)
return pd.DataFrame({col: df[col].values, "ppo": ppo})
[docs]def 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
Args:
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:
DataFrame: result
"""
df = client.chartDF(symbol, timeframe)
return pd.DataFrame(
{col: df[col].values, "roc": t.ROC(df[col].values.astype(float), period)}
)
[docs]def 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
Args:
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:
DataFrame: result
"""
df = client.chartDF(symbol, timeframe)
return pd.DataFrame(
{col: df[col].values, "rocp": t.ROCP(df[col].values.astype(float), period)}
)
[docs]def 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
Args:
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:
DataFrame: result
"""
df = client.chartDF(symbol, timeframe)
return pd.DataFrame(
{col: df[col].values, "rocr": t.ROCR(df[col].values.astype(float), period)}
)
[docs]def 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
Args:
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:
DataFrame: result
"""
df = client.chartDF(symbol, timeframe)
return pd.DataFrame(
{
col: df[col].values,
"rocr100": t.ROCR100(df[col].values.astype(float), period),
}
)
[docs]def 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
Args:
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:
DataFrame: result
"""
df = client.chartDF(symbol, timeframe)
return pd.DataFrame(
{col: df[col].values, "rsi": t.RSI(df[col].values.astype(float), period)}
)
[docs]def 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
Args:
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:
DataFrame: result
"""
df = client.chartDF(symbol, timeframe)
slowk, slowd = t.STOCH(
df[highcol].values.astype(float),
df[lowcol].values.astype(float),
df[closecol].values.astype(float),
fastk_period=fastk_period,
slowk_period=slowk_period,
slowk_matype=slowk_matype,
slowd_period=slowd_period,
slowd_matype=slowd_matype,
)
return pd.DataFrame(
{
highcol: df[highcol].values,
lowcol: df[lowcol].values,
closecol: df[closecol].values,
"slowk": slowk,
"slowd": slowd,
}
)
[docs]def 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
Args:
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:
DataFrame: result
"""
df = client.chartDF(symbol, timeframe)
fastk, fastd = t.STOCHF(
df[highcol].values.astype(float),
df[lowcol].values.astype(float),
df[closecol].values.astype(float),
fastk_period=fastk_period,
slowk_period=slowk_period,
slowk_matype=slowk_matype,
slowd_period=slowd_period,
slowd_matype=slowd_matype,
)
return pd.DataFrame(
{
highcol: df[highcol].values,
lowcol: df[lowcol].values,
closecol: df[closecol].values,
"fastk": fastk,
"fastd": fastd,
}
)
[docs]def 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
Args:
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:
DataFrame: result
"""
df = client.chartDF(symbol, timeframe)
fastk, fastd = t.STOCHRSI(
df[closecol].values.astype(float),
timeperiod=period,
fastk_period=fastk_period,
fastd_period=fastd_period,
fastd_matype=fastd_matype,
)
return pd.DataFrame({closecol: df[closecol].values, "fastk": fastk, "fastd": fastd})
[docs]def 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
Args:
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:
DataFrame: result
"""
df = client.chartDF(symbol, timeframe)
return pd.DataFrame(
{col: df[col].values, "trix": t.TRIX(df[col].values.astype(float), period)}
)
[docs]def 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
Args:
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:
DataFrame: result
"""
df = client.chartDF(symbol, timeframe)
x = t.ULTOSC(
df[highcol].values.astype(float),
df[lowcol].values.astype(float),
df[closecol].values.astype(float),
timeperiod1=period1,
timeperiod2=period2,
timeperiod3=period3,
)
return pd.DataFrame(
{
highcol: df[highcol].values,
lowcol: df[lowcol].values,
closecol: df[closecol].values,
"ultosc": x,
}
)
[docs]def 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
Args:
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:
DataFrame: result
"""
df = client.chartDF(symbol, timeframe)
x = t.WILLR(
df[highcol].values.astype(float),
df[lowcol].values.astype(float),
df[closecol].values.astype(float),
period,
)
return pd.DataFrame(
{
highcol: df[highcol].values,
lowcol: df[lowcol].values,
closecol: df[closecol].values,
"willr": x,
}
)