def WOBV(data):
"""
Weighted On Balance Volume.
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data.
:return pd.Series: with indicator data calculation results.
"""
return TA.WOBV(data)
def BASPN(data, period=40):
"""
Buy and Sell Pressure Normalized
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int period: period used for indicator calculation
:return pd.Series: with indicator data calculation results
"""
return TA.BASPN(data, period)
def EBBP(data):
"""
Bull power and bear power by Dr. Alexander Elder
EBBP show where today’s high and low lie relative to the a 13-day EMA
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:return pd.Series: with indicator data calculation results
"""
return TA.EBBP(data)
def CFI(data):
"""
Cumulative Force Index.
Adopted from Elder's Force Index (EFI).
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:return pd.Series: with indicator data calculation results
"""
return TA.CFI(data)
def VAMA(data, period=8, column='close'):
"""
Volume Adjusted Moving Average
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int period: period used for indicator calculation
:param str column: column used for indicator calculation (default = "close")
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
"""
return TA.VAMA(data, period, column)
def WTO(data, channel_length=10, average_length=21):
"""
Wave Trend Oscillator
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int channel_length: channel length value
:param int average_length: average length value
:return pd.Series: with indicator data calculation results
"""
return TA.WTO(data, channel_length, average_length)
def COPP(data):
"""
Coppock Curve
COPP is a momentum indicator, it signals buying opportunities when the indicator moved from negative territory
to positive territory.
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:return pd.Series: with indicator data calculation results
"""
return TA.COPP(data)
def BASP(data, period=40):
"""
Buy and Sell Pressure
BASP indicator serves to identify buying and selling pressure.
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int period: period used for indicator calculation
:return pd.Series: with indicator data calculation results
"""
return TA.BASP(data, period)
def STOCHD(data, period=3):
"""
Stochastic Oscillator %D
STOCH %D is a 3 period simple moving average of %K.
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int period: period used for indicator calculation
:return pd.Series: with indicator data calculation results
"""
return TA.STOCHD(data, period)
def VWAP(data):
"""
Weighted Moving Average
WMAP helps to smooth the price curve for better trend identification.
It places even greater importance on recent data than the EMA does.
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:return pd.Series: with indicator data calculation results
"""
return TA.VWAP(data)
def EFI(data, period=13):
"""
Elder Force Index
EFI is an indicator that uses price and volume to assess the power behind a move or identify possible turning
points.
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int period: period used for indicator calculation
:return pd.Series: with indicator data calculation results
"""
return TA.EFI(data, period=period)
def ICHIMOKU(data):
"""
Ichimoku indicator
The Ichimoku Cloud, also known as Ichimoku Kinko Hyo, is a versatile indicator that defines support and
resistance, identifies trend direction, gauges momentum and provides trading signals.
Ichimoku Kinko Hyo translates into “one look equilibrium chart”.
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:return pd.Series: with indicator data calculation results
"""
return TA.ICHIMOKU(data)
def TSI(data, long=25, short=13, signal=13):
"""
True Strength Index
TSI is a momentum oscillator based on a double smoothing of price changes.
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int long: long period used for indicator calculation
:param int short: short period used for indicator calculation
:param int signal: signal period used for indicator calculation
:return pd.Series: with indicator data calculation results
"""
return TA.TSI(data, long, short, signal)
def DO(data, period=20):
"""
Donchian Channel
DC is a moving average indicator developed by Richard Donchian.
It plots the highest high and lowest low over the last period time intervals.
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int period: period used for indicator calculation
:return pd.Series: with indicator data calculation results
"""
return TA.DO(data, period)
def MI(data, period=9):
"""
Mass Index
MI uses the high-low range to identify trend reversals based on range expansions.
In this sense, the Mass Index is a volatility indicator that does not have a directional bias.
Instead, the Mass Index identifies range bulges that can foreshadow a reversal of the current trend.
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int period: period used for indicator calculation
:return pd.Series: with indicator data calculation results
"""
return TA.MI(data, period=period)
def FISH(data, period=10):
"""
Fisher's Transform
FISH was presented by John Ehler's. It assumes that price distributions behave like square waves.
The Fisher Transform uses the mid-point or median price in a series of calculations to produce an oscillator.
A signal line which is a previous value of itself is also calculated.
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int period: period used for indicator calculation
:return pd.Series: with indicator data calculation results
"""
return TA.FISH(data, period)
def IFT_RSI(data, rsi_period=14, wma_period=9):
"""
Modified Inverse Fisher Transform applied on RSI.
Suggested method to use any IFT indicator is to buy when the indicator crosses over –0.5 or crosses over +0.5
if it has not previously crossed over –0.5 and to sell short when the indicators crosses under +0.5 or crosses
under –0.5 if it has not previously crossed under +0.5.
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int rsi_period: pandas DataFrame with open, high, low, close data
:param int wma_period: pandas DataFrame with open, high, low, close data
:return pd.Series: with indicator data calculation results
"""
return TA.IFT_RSI(data, rsi_period, wma_period)
def EMV(data, period=14):
"""
Ease of Movement
EMV is a volume-based oscillator that fluctuates above and below the zero line.
As its name implies, it is designed to measure the "ease" of price movement.
Prices are advancing with relative ease when the oscillator is in positive territory.
Conversely, prices are declining with relative ease when the oscillator is in negative territory.
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int period: period used for indicator calculation
:return:pd.Series: with indicator data calculation results
"""
return TA.EMV(data, period)
def CHANDELIER(data, period_1=14, period_2=22, k=3):
"""
Chandelier Exit
Chandelier Exit sets a trailing stop-loss based on the Average True Range (ATR).
The indicator is designed to keep traders in a trend and prevent an early exit as long as the trend extends.
Typically, the Chandelier Exit will be above prices during a downtrend and below prices during an uptrend.
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int period_1: first period used for indicator calculation
:param int period_2: second period used for indicator calculation
:param k:
:return pd.Series: with indicator data calculation results
"""
return TA.CHANDELIER(ohlc=data, period_1=period_1, period_2=period_2, k=k)
def APZ(data, period=21, dev_factor=2, ma_type=None):
"""
Adaptive Price Zone
APZ is as a volatility based indicator developed by Lee Leibfarth that appears as a set of bands placed over a
price chart.
Especially useful in non-trending, choppy markets, the APZ was created to help traders find potential turning
points in the markets.
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int period: period used for indicator calculation
:param float dev_factor: standard derivation factor
:param int ma_type: moving average type (0 simple, 1 exponential)
:return pd.Series: with indicator data calculation results
"""
return TA.APZ(data, period, dev_factor, ma_type)
def UO(data):
"""
Ultimate Oscillator
UO is a momentum oscillator designed to capture momentum across three different time frames.
The multiple time frame objective seeks to avoid the pitfalls of other oscillators.
Many momentum oscillators surge at the beginning of a strong advance and then form bearish divergence as the
advance continues.
This is because they are stuck with one time frame. The Ultimate Oscillator attempts to correct this fault by
incorporating longer time frames into the basic formula.
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:return pd.Series: with indicator data calculation results
"""
return TA.UO(data)
def KST(data, r1=10, r2=15, r3=20, r4=30):
"""
Know Sure Thing
KST is a momentum oscillator based on the smoothed rate-of-change for four different time frames.
KST measures price momentum for four different price cycles. It can be used just like any momentum oscillator.
Chartists can look for divergences, overbought/oversold readings, signal line crossovers and center-line
crossovers.
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int r1: period used at first ROC calculation
:param int r2: period used at second ROC calculation
:param int r3: period used at third ROC calculation
:param int r4: period used at last ROC calculation
:return pd.Series: with indicator data calculation results
"""
return TA.KST(data, r1, r2, r3, r4)
def HMA(data, period=16):
"""
Hell's Moving Average
HMA indicator is a common abbreviation of Hull Moving Average.
The average was developed by Allan Hull and is used mainly to identify the current market trend.
Unlike SMA (simple moving average) the curve of Hull moving average is considerably smoother.
Moreover, because its aim is to minimize the lag between HMA and price it does follow the price activity much
closer.
It is used especially for middle-term and long-term trading.
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int period: period used for indicator calculation
:return pd.Series: with indicator data calculation results
"""
return TA.HMA(data, period)
def VORTEX(data, period=42):
"""
Vortex Indicator
The Vortex indicator plots two oscillating lines, one to identify positive trend movement and the other
to identify negative price movement.
Indicator construction revolves around the highs and lows of the last two days or periods.
The distance from the current high to the prior low designates positive trend movement while the
distance between the current low and the prior high designates negative trend movement.
Strongly positive or negative trend movements will show a longer length between the two numbers while
weaker positive or negative trend movement will show a shorter length.
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int period: period used for indicator calculation
:return pd.Series: with indicator data calculation results
"""
return TA.VORTEX(data, period)
def VZO(data, period=14):
"""
VZO
VZO uses price, previous price and moving averages to compute its oscillating value.
It is a leading indicator that calculates buy and sell signals based on oversold / overbought conditions.
Oscillations between the 5% and 40% levels mark a bullish trend zone, while oscillations between -40% and 5%
mark a bearish trend zone.
Meanwhile, readings above 40% signal an overbought condition, while readings above 60% signal an extremely
overbought condition.
Alternatively, readings below -40% indicate an oversold condition, which becomes extremely oversold below -60%.
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int period: period used for indicator calculation
:return pd.Series: with indicator data calculation results
"""
return TA.VZO(data, period)
def KC(data, period=20):
"""
Keltner Channels
KC are volatility-based envelopes set above and below an exponential moving average.
This indicator is similar to Bollinger Bands, which use the standard deviation to set the bands.
Instead of using the standard deviation, Keltner Channels use the Average True Range (ATR) to set channel
distance.
The channels are typically set two Average True Range values above and below the 20-day EMA.
The exponential moving average dictates direction and the Average True Range sets channel width.
Keltner Channels are a trend following indicator used to identify reversals with channel breakouts and channel
direction.
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int period: period used for indicator calculation
:return pd.Series: with indicator data calculation results
"""
return TA.KC(data, period)