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GeneratedLevel2TimeArrays
femtotrader edited this page Aug 22, 2017
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1 revision
# Auto generated file
using TimeSeries
"""
ACOS(ta::TimeSeries.TimeArray, price=:Close)
Vector Trigonometric ACos (Acos)
Math Transform
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function ACOS(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = ACOS(ta[price].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
AD(ta::TimeSeries.TimeArray, price=:Close)
Chaikin A/D Line (Ad)
Volume Indicators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- High
- Low
- Close
- Volume
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function AD(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = AD(ta["High"].values, ta["Low"].values, ta["Close"].values, ta["Volume"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
ADD(ta::TimeSeries.TimeArray, ta2::TimeSeries.TimeArray, price=:Close)
Vector Arithmetic Add (Add)
Math Operators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
- ta2::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function ADD(ta::TimeSeries.TimeArray, ta2::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = ADD(ta[price].values, ta2[price].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
ADOSC(ta::TimeSeries.TimeArray; fast_period=Integer(3), slow_period=Integer(10), price=:Close)
Chaikin A/D Oscillator (AdOsc)
Volume Indicators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- High
- Low
- Close
- Volume
OptionalInputArguments:
- fast_period=Integer(3)
- slow_period=Integer(10)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function ADOSC(ta::TimeSeries.TimeArray; fast_period=Integer(3), slow_period=Integer(10), price=:Close)
price = string(price)
result = ADOSC(ta["High"].values, ta["Low"].values, ta["Close"].values, ta["Volume"].values, fast_period=fast_period, slow_period=slow_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
ADX(ta::TimeSeries.TimeArray; time_period=Integer(14), price=:Close)
Average Directional Movement Index (Adx)
Momentum Indicators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- High
- Low
- Close
OptionalInputArguments:
- time_period=Integer(14)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function ADX(ta::TimeSeries.TimeArray; time_period=Integer(14), price=:Close)
price = string(price)
result = ADX(ta["High"].values, ta["Low"].values, ta["Close"].values, time_period=time_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
ADXR(ta::TimeSeries.TimeArray; time_period=Integer(14), price=:Close)
Average Directional Movement Index Rating (Adxr)
Momentum Indicators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- High
- Low
- Close
OptionalInputArguments:
- time_period=Integer(14)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function ADXR(ta::TimeSeries.TimeArray; time_period=Integer(14), price=:Close)
price = string(price)
result = ADXR(ta["High"].values, ta["Low"].values, ta["Close"].values, time_period=time_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
APO(ta::TimeSeries.TimeArray; fast_period=Integer(12), slow_period=Integer(26), ma_type=TA_MAType(0), price=:Close)
Absolute Price Oscillator (Apo)
Momentum Indicators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
- fast_period=Integer(12)
- slow_period=Integer(26)
- ma_type=TA_MAType(0)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function APO(ta::TimeSeries.TimeArray; fast_period=Integer(12), slow_period=Integer(26), ma_type=TA_MAType(0), price=:Close)
price = string(price)
result = APO(ta[price].values, fast_period=fast_period, slow_period=slow_period, ma_type=ma_type)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
AROON(ta::TimeSeries.TimeArray; time_period=Integer(14), price=:Close)
Aroon (Aroon)
Momentum Indicators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- High
- Low
OptionalInputArguments:
- time_period=Integer(14)
Returns:
- TimeSeries.TimeArray with:
- outAroonDown
- outAroonUp
"""
function AROON(ta::TimeSeries.TimeArray; time_period=Integer(14), price=:Close)
price = string(price)
result = AROON(ta["High"].values, ta["Low"].values, time_period=time_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["outAroonDown", "outAroonUp"])
out
end
"""
AROONOSC(ta::TimeSeries.TimeArray; time_period=Integer(14), price=:Close)
Aroon Oscillator (AroonOsc)
Momentum Indicators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- High
- Low
OptionalInputArguments:
- time_period=Integer(14)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function AROONOSC(ta::TimeSeries.TimeArray; time_period=Integer(14), price=:Close)
price = string(price)
result = AROONOSC(ta["High"].values, ta["Low"].values, time_period=time_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
ASIN(ta::TimeSeries.TimeArray, price=:Close)
Vector Trigonometric ASin (Asin)
Math Transform
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function ASIN(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = ASIN(ta[price].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
ATAN(ta::TimeSeries.TimeArray, price=:Close)
Vector Trigonometric ATan (Atan)
Math Transform
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function ATAN(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = ATAN(ta[price].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
ATR(ta::TimeSeries.TimeArray; time_period=Integer(14), price=:Close)
Average True Range (Atr)
Volatility Indicators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- High
- Low
- Close
OptionalInputArguments:
- time_period=Integer(14)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function ATR(ta::TimeSeries.TimeArray; time_period=Integer(14), price=:Close)
price = string(price)
result = ATR(ta["High"].values, ta["Low"].values, ta["Close"].values, time_period=time_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
AVGPRICE(ta::TimeSeries.TimeArray, price=:Close)
Average Price (AvgPrice)
Price Transform
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function AVGPRICE(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = AVGPRICE(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
BBANDS(ta::TimeSeries.TimeArray; time_period=Integer(5), deviations_up=AbstractFloat(2.000000e+0), deviations_down=AbstractFloat(2.000000e+0), ma_type=TA_MAType(0), price=:Close)
Bollinger Bands (Bbands)
Overlap Studies
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
- time_period=Integer(5)
- deviations_up=AbstractFloat(2.000000e+0)
- deviations_down=AbstractFloat(2.000000e+0)
- ma_type=TA_MAType(0)
Returns:
- TimeSeries.TimeArray with:
- UpperBand
- MiddleBand
- LowerBand
"""
function BBANDS(ta::TimeSeries.TimeArray; time_period=Integer(5), deviations_up=AbstractFloat(2.000000e+0), deviations_down=AbstractFloat(2.000000e+0), ma_type=TA_MAType(0), price=:Close)
price = string(price)
result = BBANDS(ta[price].values, time_period=time_period, deviations_up=deviations_up, deviations_down=deviations_down, ma_type=ma_type)
dt = ta.timestamp
out = TimeArray(dt, result, String["UpperBand", "MiddleBand", "LowerBand"])
out
end
"""
BETA(ta::TimeSeries.TimeArray, ta2::TimeSeries.TimeArray; time_period=Integer(5), price=:Close)
Beta (Beta)
Statistic Functions
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
- ta2::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
- time_period=Integer(5)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function BETA(ta::TimeSeries.TimeArray, ta2::TimeSeries.TimeArray; time_period=Integer(5), price=:Close)
price = string(price)
result = BETA(ta[price].values, ta2[price].values, time_period=time_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
BOP(ta::TimeSeries.TimeArray, price=:Close)
Balance Of Power (Bop)
Momentum Indicators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function BOP(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = BOP(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
CCI(ta::TimeSeries.TimeArray; time_period=Integer(14), price=:Close)
Commodity Channel Index (Cci)
Momentum Indicators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- High
- Low
- Close
OptionalInputArguments:
- time_period=Integer(14)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function CCI(ta::TimeSeries.TimeArray; time_period=Integer(14), price=:Close)
price = string(price)
result = CCI(ta["High"].values, ta["Low"].values, ta["Close"].values, time_period=time_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
CDL2CROWS(ta::TimeSeries.TimeArray, price=:Close)
Two Crows (Cdl2Crows)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDL2CROWS(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDL2CROWS(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDL3BLACKCROWS(ta::TimeSeries.TimeArray, price=:Close)
Three Black Crows (Cdl3BlackCrows)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDL3BLACKCROWS(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDL3BLACKCROWS(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDL3INSIDE(ta::TimeSeries.TimeArray, price=:Close)
Three Inside Up/Down (Cdl3Inside)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDL3INSIDE(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDL3INSIDE(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDL3LINESTRIKE(ta::TimeSeries.TimeArray, price=:Close)
Three-Line Strike (Cdl3LineStrike)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDL3LINESTRIKE(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDL3LINESTRIKE(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDL3OUTSIDE(ta::TimeSeries.TimeArray, price=:Close)
Three Outside Up/Down (Cdl3Outside)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDL3OUTSIDE(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDL3OUTSIDE(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDL3STARSINSOUTH(ta::TimeSeries.TimeArray, price=:Close)
Three Stars In The South (Cdl3StarsInSouth)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDL3STARSINSOUTH(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDL3STARSINSOUTH(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDL3WHITESOLDIERS(ta::TimeSeries.TimeArray, price=:Close)
Three Advancing White Soldiers (Cdl3WhiteSoldiers)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDL3WHITESOLDIERS(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDL3WHITESOLDIERS(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLABANDONEDBABY(ta::TimeSeries.TimeArray; penetration=AbstractFloat(3.000000e-1), price=:Close)
Abandoned Baby (CdlAbandonedBaby)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
- penetration=AbstractFloat(3.000000e-1)
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLABANDONEDBABY(ta::TimeSeries.TimeArray; penetration=AbstractFloat(3.000000e-1), price=:Close)
price = string(price)
result = CDLABANDONEDBABY(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, penetration=penetration)
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLADVANCEBLOCK(ta::TimeSeries.TimeArray, price=:Close)
Advance Block (CdlAdvanceBlock)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLADVANCEBLOCK(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDLADVANCEBLOCK(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLBELTHOLD(ta::TimeSeries.TimeArray, price=:Close)
Belt-hold (CdlBeltHold)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLBELTHOLD(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDLBELTHOLD(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLBREAKAWAY(ta::TimeSeries.TimeArray, price=:Close)
Breakaway (CdlBreakaway)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLBREAKAWAY(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDLBREAKAWAY(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLCLOSINGMARUBOZU(ta::TimeSeries.TimeArray, price=:Close)
Closing Marubozu (CdlClosingMarubozu)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLCLOSINGMARUBOZU(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDLCLOSINGMARUBOZU(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLCONCEALBABYSWALL(ta::TimeSeries.TimeArray, price=:Close)
Concealing Baby Swallow (CdlConcealBabysWall)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLCONCEALBABYSWALL(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDLCONCEALBABYSWALL(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLCOUNTERATTACK(ta::TimeSeries.TimeArray, price=:Close)
Counterattack (CdlCounterAttack)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLCOUNTERATTACK(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDLCOUNTERATTACK(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLDARKCLOUDCOVER(ta::TimeSeries.TimeArray; penetration=AbstractFloat(5.000000e-1), price=:Close)
Dark Cloud Cover (CdlDarkCloudCover)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
- penetration=AbstractFloat(5.000000e-1)
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLDARKCLOUDCOVER(ta::TimeSeries.TimeArray; penetration=AbstractFloat(5.000000e-1), price=:Close)
price = string(price)
result = CDLDARKCLOUDCOVER(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, penetration=penetration)
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLDOJI(ta::TimeSeries.TimeArray, price=:Close)
Doji (CdlDoji)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLDOJI(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDLDOJI(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLDOJISTAR(ta::TimeSeries.TimeArray, price=:Close)
Doji Star (CdlDojiStar)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLDOJISTAR(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDLDOJISTAR(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLDRAGONFLYDOJI(ta::TimeSeries.TimeArray, price=:Close)
Dragonfly Doji (CdlDragonflyDoji)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLDRAGONFLYDOJI(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDLDRAGONFLYDOJI(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLENGULFING(ta::TimeSeries.TimeArray, price=:Close)
Engulfing Pattern (CdlEngulfing)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLENGULFING(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDLENGULFING(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLEVENINGDOJISTAR(ta::TimeSeries.TimeArray; penetration=AbstractFloat(3.000000e-1), price=:Close)
Evening Doji Star (CdlEveningDojiStar)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
- penetration=AbstractFloat(3.000000e-1)
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLEVENINGDOJISTAR(ta::TimeSeries.TimeArray; penetration=AbstractFloat(3.000000e-1), price=:Close)
price = string(price)
result = CDLEVENINGDOJISTAR(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, penetration=penetration)
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLEVENINGSTAR(ta::TimeSeries.TimeArray; penetration=AbstractFloat(3.000000e-1), price=:Close)
Evening Star (CdlEveningStar)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
- penetration=AbstractFloat(3.000000e-1)
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLEVENINGSTAR(ta::TimeSeries.TimeArray; penetration=AbstractFloat(3.000000e-1), price=:Close)
price = string(price)
result = CDLEVENINGSTAR(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, penetration=penetration)
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLGAPSIDESIDEWHITE(ta::TimeSeries.TimeArray, price=:Close)
Up/Down-gap side-by-side white lines (CdlGapSideSideWhite)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLGAPSIDESIDEWHITE(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDLGAPSIDESIDEWHITE(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLGRAVESTONEDOJI(ta::TimeSeries.TimeArray, price=:Close)
Gravestone Doji (CdlGravestoneDoji)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLGRAVESTONEDOJI(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDLGRAVESTONEDOJI(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLHAMMER(ta::TimeSeries.TimeArray, price=:Close)
Hammer (CdlHammer)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLHAMMER(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDLHAMMER(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLHANGINGMAN(ta::TimeSeries.TimeArray, price=:Close)
Hanging Man (CdlHangingMan)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLHANGINGMAN(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDLHANGINGMAN(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLHARAMI(ta::TimeSeries.TimeArray, price=:Close)
Harami Pattern (CdlHarami)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLHARAMI(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDLHARAMI(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLHARAMICROSS(ta::TimeSeries.TimeArray, price=:Close)
Harami Cross Pattern (CdlHaramiCross)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLHARAMICROSS(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDLHARAMICROSS(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLHIGHWAVE(ta::TimeSeries.TimeArray, price=:Close)
High-Wave Candle (CdlHignWave)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLHIGHWAVE(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDLHIGHWAVE(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLHIKKAKE(ta::TimeSeries.TimeArray, price=:Close)
Hikkake Pattern (CdlHikkake)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLHIKKAKE(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDLHIKKAKE(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLHIKKAKEMOD(ta::TimeSeries.TimeArray, price=:Close)
Modified Hikkake Pattern (CdlHikkakeMod)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLHIKKAKEMOD(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDLHIKKAKEMOD(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLHOMINGPIGEON(ta::TimeSeries.TimeArray, price=:Close)
Homing Pigeon (CdlHomingPigeon)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLHOMINGPIGEON(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDLHOMINGPIGEON(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLIDENTICAL3CROWS(ta::TimeSeries.TimeArray, price=:Close)
Identical Three Crows (CdlIdentical3Crows)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLIDENTICAL3CROWS(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDLIDENTICAL3CROWS(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLINNECK(ta::TimeSeries.TimeArray, price=:Close)
In-Neck Pattern (CdlInNeck)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLINNECK(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDLINNECK(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLINVERTEDHAMMER(ta::TimeSeries.TimeArray, price=:Close)
Inverted Hammer (CdlInvertedHammer)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLINVERTEDHAMMER(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDLINVERTEDHAMMER(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLKICKING(ta::TimeSeries.TimeArray, price=:Close)
Kicking (CdlKicking)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLKICKING(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDLKICKING(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLKICKINGBYLENGTH(ta::TimeSeries.TimeArray, price=:Close)
Kicking - bull/bear determined by the longer marubozu (CdlKickingByLength)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLKICKINGBYLENGTH(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDLKICKINGBYLENGTH(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLLADDERBOTTOM(ta::TimeSeries.TimeArray, price=:Close)
Ladder Bottom (CdlLadderBottom)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLLADDERBOTTOM(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDLLADDERBOTTOM(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLLONGLEGGEDDOJI(ta::TimeSeries.TimeArray, price=:Close)
Long Legged Doji (CdlLongLeggedDoji)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLLONGLEGGEDDOJI(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDLLONGLEGGEDDOJI(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLLONGLINE(ta::TimeSeries.TimeArray, price=:Close)
Long Line Candle (CdlLongLine)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLLONGLINE(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDLLONGLINE(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLMARUBOZU(ta::TimeSeries.TimeArray, price=:Close)
Marubozu (CdlMarubozu)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLMARUBOZU(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDLMARUBOZU(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLMATCHINGLOW(ta::TimeSeries.TimeArray, price=:Close)
Matching Low (CdlMatchingLow)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLMATCHINGLOW(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDLMATCHINGLOW(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLMATHOLD(ta::TimeSeries.TimeArray; penetration=AbstractFloat(5.000000e-1), price=:Close)
Mat Hold (CdlMatHold)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
- penetration=AbstractFloat(5.000000e-1)
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLMATHOLD(ta::TimeSeries.TimeArray; penetration=AbstractFloat(5.000000e-1), price=:Close)
price = string(price)
result = CDLMATHOLD(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, penetration=penetration)
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLMORNINGDOJISTAR(ta::TimeSeries.TimeArray; penetration=AbstractFloat(3.000000e-1), price=:Close)
Morning Doji Star (CdlMorningDojiStar)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
- penetration=AbstractFloat(3.000000e-1)
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLMORNINGDOJISTAR(ta::TimeSeries.TimeArray; penetration=AbstractFloat(3.000000e-1), price=:Close)
price = string(price)
result = CDLMORNINGDOJISTAR(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, penetration=penetration)
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLMORNINGSTAR(ta::TimeSeries.TimeArray; penetration=AbstractFloat(3.000000e-1), price=:Close)
Morning Star (CdlMorningStar)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
- penetration=AbstractFloat(3.000000e-1)
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLMORNINGSTAR(ta::TimeSeries.TimeArray; penetration=AbstractFloat(3.000000e-1), price=:Close)
price = string(price)
result = CDLMORNINGSTAR(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, penetration=penetration)
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLONNECK(ta::TimeSeries.TimeArray, price=:Close)
On-Neck Pattern (CdlOnNeck)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLONNECK(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDLONNECK(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLPIERCING(ta::TimeSeries.TimeArray, price=:Close)
Piercing Pattern (CdlPiercing)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLPIERCING(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDLPIERCING(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLRICKSHAWMAN(ta::TimeSeries.TimeArray, price=:Close)
Rickshaw Man (CdlRickshawMan)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLRICKSHAWMAN(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDLRICKSHAWMAN(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLRISEFALL3METHODS(ta::TimeSeries.TimeArray, price=:Close)
Rising/Falling Three Methods (CdlRiseFall3Methods)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLRISEFALL3METHODS(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDLRISEFALL3METHODS(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLSEPARATINGLINES(ta::TimeSeries.TimeArray, price=:Close)
Separating Lines (CdlSeperatingLines)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLSEPARATINGLINES(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDLSEPARATINGLINES(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLSHOOTINGSTAR(ta::TimeSeries.TimeArray, price=:Close)
Shooting Star (CdlShootingStar)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLSHOOTINGSTAR(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDLSHOOTINGSTAR(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLSHORTLINE(ta::TimeSeries.TimeArray, price=:Close)
Short Line Candle (CdlShortLine)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLSHORTLINE(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDLSHORTLINE(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLSPINNINGTOP(ta::TimeSeries.TimeArray, price=:Close)
Spinning Top (CdlSpinningTop)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLSPINNINGTOP(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDLSPINNINGTOP(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLSTALLEDPATTERN(ta::TimeSeries.TimeArray, price=:Close)
Stalled Pattern (CdlStalledPattern)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLSTALLEDPATTERN(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDLSTALLEDPATTERN(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLSTICKSANDWICH(ta::TimeSeries.TimeArray, price=:Close)
Stick Sandwich (CdlStickSandwhich)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLSTICKSANDWICH(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDLSTICKSANDWICH(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLTAKURI(ta::TimeSeries.TimeArray, price=:Close)
Takuri (Dragonfly Doji with very long lower shadow) (CdlTakuri)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLTAKURI(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDLTAKURI(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLTASUKIGAP(ta::TimeSeries.TimeArray, price=:Close)
Tasuki Gap (CdlTasukiGap)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLTASUKIGAP(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDLTASUKIGAP(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLTHRUSTING(ta::TimeSeries.TimeArray, price=:Close)
Thrusting Pattern (CdlThrusting)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLTHRUSTING(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDLTHRUSTING(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLTRISTAR(ta::TimeSeries.TimeArray, price=:Close)
Tristar Pattern (CdlTristar)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLTRISTAR(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDLTRISTAR(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLUNIQUE3RIVER(ta::TimeSeries.TimeArray, price=:Close)
Unique 3 River (CdlUnique3River)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLUNIQUE3RIVER(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDLUNIQUE3RIVER(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLUPSIDEGAP2CROWS(ta::TimeSeries.TimeArray, price=:Close)
Upside Gap Two Crows (CdlUpsideGap2Crows)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLUPSIDEGAP2CROWS(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDLUPSIDEGAP2CROWS(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CDLXSIDEGAP3METHODS(ta::TimeSeries.TimeArray, price=:Close)
Upside/Downside Gap Three Methods (CdlXSideGap3Methods)
Pattern Recognition
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- Open
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function CDLXSIDEGAP3METHODS(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CDLXSIDEGAP3METHODS(ta["Open"].values, ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
CEIL(ta::TimeSeries.TimeArray, price=:Close)
Vector Ceil (Ceil)
Math Transform
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function CEIL(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = CEIL(ta[price].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
CMO(ta::TimeSeries.TimeArray; time_period=Integer(14), price=:Close)
Chande Momentum Oscillator (Cmo)
Momentum Indicators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
- time_period=Integer(14)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function CMO(ta::TimeSeries.TimeArray; time_period=Integer(14), price=:Close)
price = string(price)
result = CMO(ta[price].values, time_period=time_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
CORREL(ta::TimeSeries.TimeArray, ta2::TimeSeries.TimeArray; time_period=Integer(30), price=:Close)
Pearson's Correlation Coefficient (r) (Correl)
Statistic Functions
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
- ta2::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
- time_period=Integer(30)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function CORREL(ta::TimeSeries.TimeArray, ta2::TimeSeries.TimeArray; time_period=Integer(30), price=:Close)
price = string(price)
result = CORREL(ta[price].values, ta2[price].values, time_period=time_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
COS(ta::TimeSeries.TimeArray, price=:Close)
Vector Trigonometric Cos (Cos)
Math Transform
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function COS(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = COS(ta[price].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
COSH(ta::TimeSeries.TimeArray, price=:Close)
Vector Trigonometric Cosh (Cosh)
Math Transform
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function COSH(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = COSH(ta[price].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
DEMA(ta::TimeSeries.TimeArray; time_period=Integer(30), price=:Close)
Double Exponential Moving Average (Dema)
Overlap Studies
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
- time_period=Integer(30)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function DEMA(ta::TimeSeries.TimeArray; time_period=Integer(30), price=:Close)
price = string(price)
result = DEMA(ta[price].values, time_period=time_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
DIV(ta::TimeSeries.TimeArray, ta2::TimeSeries.TimeArray, price=:Close)
Vector Arithmetic Div (Div)
Math Operators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
- ta2::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function DIV(ta::TimeSeries.TimeArray, ta2::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = DIV(ta[price].values, ta2[price].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
DX(ta::TimeSeries.TimeArray; time_period=Integer(14), price=:Close)
Directional Movement Index (Dx)
Momentum Indicators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- High
- Low
- Close
OptionalInputArguments:
- time_period=Integer(14)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function DX(ta::TimeSeries.TimeArray; time_period=Integer(14), price=:Close)
price = string(price)
result = DX(ta["High"].values, ta["Low"].values, ta["Close"].values, time_period=time_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
EMA(ta::TimeSeries.TimeArray; time_period=Integer(30), price=:Close)
Exponential Moving Average (Ema)
Overlap Studies
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
- time_period=Integer(30)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function EMA(ta::TimeSeries.TimeArray; time_period=Integer(30), price=:Close)
price = string(price)
result = EMA(ta[price].values, time_period=time_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
EXP(ta::TimeSeries.TimeArray, price=:Close)
Vector Arithmetic Exp (Exp)
Math Transform
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function EXP(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = EXP(ta[price].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
FLOOR(ta::TimeSeries.TimeArray, price=:Close)
Vector Floor (Floor)
Math Transform
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function FLOOR(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = FLOOR(ta[price].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
HT_DCPERIOD(ta::TimeSeries.TimeArray, price=:Close)
Hilbert Transform - Dominant Cycle Period (HtDcPeriod)
Cycle Indicators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function HT_DCPERIOD(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = HT_DCPERIOD(ta[price].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
HT_DCPHASE(ta::TimeSeries.TimeArray, price=:Close)
Hilbert Transform - Dominant Cycle Phase (HtDcPhase)
Cycle Indicators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function HT_DCPHASE(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = HT_DCPHASE(ta[price].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
HT_PHASOR(ta::TimeSeries.TimeArray, price=:Close)
Hilbert Transform - Phasor Components (HtPhasor)
Cycle Indicators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- outInPhase
- outQuadrature
"""
function HT_PHASOR(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = HT_PHASOR(ta[price].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["outInPhase", "outQuadrature"])
out
end
"""
HT_SINE(ta::TimeSeries.TimeArray, price=:Close)
Hilbert Transform - SineWave (HtSine)
Cycle Indicators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- outSine
- outLeadSine
"""
function HT_SINE(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = HT_SINE(ta[price].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["outSine", "outLeadSine"])
out
end
"""
HT_TRENDLINE(ta::TimeSeries.TimeArray, price=:Close)
Hilbert Transform - Instantaneous Trendline (HtTrendline)
Overlap Studies
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function HT_TRENDLINE(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = HT_TRENDLINE(ta[price].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
HT_TRENDMODE(ta::TimeSeries.TimeArray, price=:Close)
Hilbert Transform - Trend vs Cycle Mode (HtTrendMode)
Cycle Indicators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function HT_TRENDMODE(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = HT_TRENDMODE(ta[price].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
KAMA(ta::TimeSeries.TimeArray; time_period=Integer(30), price=:Close)
Kaufman Adaptive Moving Average (Kama)
Overlap Studies
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
- time_period=Integer(30)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function KAMA(ta::TimeSeries.TimeArray; time_period=Integer(30), price=:Close)
price = string(price)
result = KAMA(ta[price].values, time_period=time_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
LINEARREG(ta::TimeSeries.TimeArray; time_period=Integer(14), price=:Close)
Linear Regression (LinearReg)
Statistic Functions
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
- time_period=Integer(14)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function LINEARREG(ta::TimeSeries.TimeArray; time_period=Integer(14), price=:Close)
price = string(price)
result = LINEARREG(ta[price].values, time_period=time_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
LINEARREG_ANGLE(ta::TimeSeries.TimeArray; time_period=Integer(14), price=:Close)
Linear Regression Angle (LinearRegAngle)
Statistic Functions
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
- time_period=Integer(14)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function LINEARREG_ANGLE(ta::TimeSeries.TimeArray; time_period=Integer(14), price=:Close)
price = string(price)
result = LINEARREG_ANGLE(ta[price].values, time_period=time_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
LINEARREG_INTERCEPT(ta::TimeSeries.TimeArray; time_period=Integer(14), price=:Close)
Linear Regression Intercept (LinearRegIntercept)
Statistic Functions
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
- time_period=Integer(14)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function LINEARREG_INTERCEPT(ta::TimeSeries.TimeArray; time_period=Integer(14), price=:Close)
price = string(price)
result = LINEARREG_INTERCEPT(ta[price].values, time_period=time_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
LINEARREG_SLOPE(ta::TimeSeries.TimeArray; time_period=Integer(14), price=:Close)
Linear Regression Slope (LinearRegSlope)
Statistic Functions
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
- time_period=Integer(14)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function LINEARREG_SLOPE(ta::TimeSeries.TimeArray; time_period=Integer(14), price=:Close)
price = string(price)
result = LINEARREG_SLOPE(ta[price].values, time_period=time_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
LN(ta::TimeSeries.TimeArray, price=:Close)
Vector Log Natural (Ln)
Math Transform
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function LN(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = LN(ta[price].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
LOG10(ta::TimeSeries.TimeArray, price=:Close)
Vector Log10 (Log10)
Math Transform
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function LOG10(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = LOG10(ta[price].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
MA(ta::TimeSeries.TimeArray; time_period=Integer(30), ma_type=TA_MAType(0), price=:Close)
Moving average (MovingAverage)
Overlap Studies
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
- time_period=Integer(30)
- ma_type=TA_MAType(0)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function MA(ta::TimeSeries.TimeArray; time_period=Integer(30), ma_type=TA_MAType(0), price=:Close)
price = string(price)
result = MA(ta[price].values, time_period=time_period, ma_type=ma_type)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
MACD(ta::TimeSeries.TimeArray; fast_period=Integer(12), slow_period=Integer(26), signal_period=Integer(9), price=:Close)
Moving Average Convergence/Divergence (Macd)
Momentum Indicators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
- fast_period=Integer(12)
- slow_period=Integer(26)
- signal_period=Integer(9)
Returns:
- TimeSeries.TimeArray with:
- outMACD
- outMACDSignal
- outMACDHist
"""
function MACD(ta::TimeSeries.TimeArray; fast_period=Integer(12), slow_period=Integer(26), signal_period=Integer(9), price=:Close)
price = string(price)
result = MACD(ta[price].values, fast_period=fast_period, slow_period=slow_period, signal_period=signal_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["outMACD", "outMACDSignal", "outMACDHist"])
out
end
"""
MACDEXT(ta::TimeSeries.TimeArray; fast_period=Integer(12), fast_ma=TA_MAType(0), slow_period=Integer(26), slow_ma=TA_MAType(0), signal_period=Integer(9), signal_ma=TA_MAType(0), price=:Close)
MACD with controllable MA type (MacdExt)
Momentum Indicators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
- fast_period=Integer(12)
- fast_ma=TA_MAType(0)
- slow_period=Integer(26)
- slow_ma=TA_MAType(0)
- signal_period=Integer(9)
- signal_ma=TA_MAType(0)
Returns:
- TimeSeries.TimeArray with:
- outMACD
- outMACDSignal
- outMACDHist
"""
function MACDEXT(ta::TimeSeries.TimeArray; fast_period=Integer(12), fast_ma=TA_MAType(0), slow_period=Integer(26), slow_ma=TA_MAType(0), signal_period=Integer(9), signal_ma=TA_MAType(0), price=:Close)
price = string(price)
result = MACDEXT(ta[price].values, fast_period=fast_period, fast_ma=fast_ma, slow_period=slow_period, slow_ma=slow_ma, signal_period=signal_period, signal_ma=signal_ma)
dt = ta.timestamp
out = TimeArray(dt, result, String["outMACD", "outMACDSignal", "outMACDHist"])
out
end
"""
MACDFIX(ta::TimeSeries.TimeArray; signal_period=Integer(9), price=:Close)
Moving Average Convergence/Divergence Fix 12/26 (MacdFix)
Momentum Indicators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
- signal_period=Integer(9)
Returns:
- TimeSeries.TimeArray with:
- outMACD
- outMACDSignal
- outMACDHist
"""
function MACDFIX(ta::TimeSeries.TimeArray; signal_period=Integer(9), price=:Close)
price = string(price)
result = MACDFIX(ta[price].values, signal_period=signal_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["outMACD", "outMACDSignal", "outMACDHist"])
out
end
"""
MAMA(ta::TimeSeries.TimeArray; fast_limit=AbstractFloat(5.000000e-1), slow_limit=AbstractFloat(5.000000e-2), price=:Close)
MESA Adaptive Moving Average (Mama)
Overlap Studies
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
- fast_limit=AbstractFloat(5.000000e-1)
- slow_limit=AbstractFloat(5.000000e-2)
Returns:
- TimeSeries.TimeArray with:
- outMAMA
- outFAMA
"""
function MAMA(ta::TimeSeries.TimeArray; fast_limit=AbstractFloat(5.000000e-1), slow_limit=AbstractFloat(5.000000e-2), price=:Close)
price = string(price)
result = MAMA(ta[price].values, fast_limit=fast_limit, slow_limit=slow_limit)
dt = ta.timestamp
out = TimeArray(dt, result, String["outMAMA", "outFAMA"])
out
end
"""
MAVP(ta::TimeSeries.TimeArray; minimum_period=Integer(2), maximum_period=Integer(30), ma_type=TA_MAType(0), price=:Close)
Moving average with variable period (MovingAverageVariablePeriod)
Overlap Studies
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
- inPeriods
OptionalInputArguments:
- minimum_period=Integer(2)
- maximum_period=Integer(30)
- ma_type=TA_MAType(0)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function MAVP(ta::TimeSeries.TimeArray; minimum_period=Integer(2), maximum_period=Integer(30), ma_type=TA_MAType(0), price=:Close)
price = string(price)
result = MAVP(ta[price].values, ta["inPeriods"].values, minimum_period=minimum_period, maximum_period=maximum_period, ma_type=ma_type)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
MAX(ta::TimeSeries.TimeArray; time_period=Integer(30), price=:Close)
Highest value over a specified period (Max)
Math Operators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
- time_period=Integer(30)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function MAX(ta::TimeSeries.TimeArray; time_period=Integer(30), price=:Close)
price = string(price)
result = MAX(ta[price].values, time_period=time_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
MAXINDEX(ta::TimeSeries.TimeArray; time_period=Integer(30), price=:Close)
Index of highest value over a specified period (MaxIndex)
Math Operators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
- time_period=Integer(30)
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function MAXINDEX(ta::TimeSeries.TimeArray; time_period=Integer(30), price=:Close)
price = string(price)
result = MAXINDEX(ta[price].values, time_period=time_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
MEDPRICE(ta::TimeSeries.TimeArray, price=:Close)
Median Price (MedPrice)
Price Transform
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- High
- Low
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function MEDPRICE(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = MEDPRICE(ta["High"].values, ta["Low"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
MFI(ta::TimeSeries.TimeArray; time_period=Integer(14), price=:Close)
Money Flow Index (Mfi)
Momentum Indicators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- High
- Low
- Close
- Volume
OptionalInputArguments:
- time_period=Integer(14)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function MFI(ta::TimeSeries.TimeArray; time_period=Integer(14), price=:Close)
price = string(price)
result = MFI(ta["High"].values, ta["Low"].values, ta["Close"].values, ta["Volume"].values, time_period=time_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
MIDPOINT(ta::TimeSeries.TimeArray; time_period=Integer(14), price=:Close)
MidPoint over period (MidPoint)
Overlap Studies
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
- time_period=Integer(14)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function MIDPOINT(ta::TimeSeries.TimeArray; time_period=Integer(14), price=:Close)
price = string(price)
result = MIDPOINT(ta[price].values, time_period=time_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
MIDPRICE(ta::TimeSeries.TimeArray; time_period=Integer(14), price=:Close)
Midpoint Price over period (MidPrice)
Overlap Studies
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- High
- Low
OptionalInputArguments:
- time_period=Integer(14)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function MIDPRICE(ta::TimeSeries.TimeArray; time_period=Integer(14), price=:Close)
price = string(price)
result = MIDPRICE(ta["High"].values, ta["Low"].values, time_period=time_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
MIN(ta::TimeSeries.TimeArray; time_period=Integer(30), price=:Close)
Lowest value over a specified period (Min)
Math Operators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
- time_period=Integer(30)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function MIN(ta::TimeSeries.TimeArray; time_period=Integer(30), price=:Close)
price = string(price)
result = MIN(ta[price].values, time_period=time_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
MININDEX(ta::TimeSeries.TimeArray; time_period=Integer(30), price=:Close)
Index of lowest value over a specified period (MinIndex)
Math Operators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
- time_period=Integer(30)
Returns:
- TimeSeries.TimeArray with:
- Integer
"""
function MININDEX(ta::TimeSeries.TimeArray; time_period=Integer(30), price=:Close)
price = string(price)
result = MININDEX(ta[price].values, time_period=time_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["Integer"])
out
end
"""
MINMAX(ta::TimeSeries.TimeArray; time_period=Integer(30), price=:Close)
Lowest and highest values over a specified period (MinMax)
Math Operators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
- time_period=Integer(30)
Returns:
- TimeSeries.TimeArray with:
- outMin
- outMax
"""
function MINMAX(ta::TimeSeries.TimeArray; time_period=Integer(30), price=:Close)
price = string(price)
result = MINMAX(ta[price].values, time_period=time_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["outMin", "outMax"])
out
end
"""
MINMAXINDEX(ta::TimeSeries.TimeArray; time_period=Integer(30), price=:Close)
Indexes of lowest and highest values over a specified period (MinMaxIndex)
Math Operators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
- time_period=Integer(30)
Returns:
- TimeSeries.TimeArray with:
- outMinIdx
- outMaxIdx
"""
function MINMAXINDEX(ta::TimeSeries.TimeArray; time_period=Integer(30), price=:Close)
price = string(price)
result = MINMAXINDEX(ta[price].values, time_period=time_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["outMinIdx", "outMaxIdx"])
out
end
"""
MINUS_DI(ta::TimeSeries.TimeArray; time_period=Integer(14), price=:Close)
Minus Directional Indicator (MinusDI)
Momentum Indicators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- High
- Low
- Close
OptionalInputArguments:
- time_period=Integer(14)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function MINUS_DI(ta::TimeSeries.TimeArray; time_period=Integer(14), price=:Close)
price = string(price)
result = MINUS_DI(ta["High"].values, ta["Low"].values, ta["Close"].values, time_period=time_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
MINUS_DM(ta::TimeSeries.TimeArray; time_period=Integer(14), price=:Close)
Minus Directional Movement (MinusDM)
Momentum Indicators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- High
- Low
OptionalInputArguments:
- time_period=Integer(14)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function MINUS_DM(ta::TimeSeries.TimeArray; time_period=Integer(14), price=:Close)
price = string(price)
result = MINUS_DM(ta["High"].values, ta["Low"].values, time_period=time_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
MOM(ta::TimeSeries.TimeArray; time_period=Integer(10), price=:Close)
Momentum (Mom)
Momentum Indicators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
- time_period=Integer(10)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function MOM(ta::TimeSeries.TimeArray; time_period=Integer(10), price=:Close)
price = string(price)
result = MOM(ta[price].values, time_period=time_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
MULT(ta::TimeSeries.TimeArray, ta2::TimeSeries.TimeArray, price=:Close)
Vector Arithmetic Mult (Mult)
Math Operators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
- ta2::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function MULT(ta::TimeSeries.TimeArray, ta2::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = MULT(ta[price].values, ta2[price].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
NATR(ta::TimeSeries.TimeArray; time_period=Integer(14), price=:Close)
Normalized Average True Range (Natr)
Volatility Indicators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- High
- Low
- Close
OptionalInputArguments:
- time_period=Integer(14)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function NATR(ta::TimeSeries.TimeArray; time_period=Integer(14), price=:Close)
price = string(price)
result = NATR(ta["High"].values, ta["Low"].values, ta["Close"].values, time_period=time_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
OBV(ta::TimeSeries.TimeArray, price=:Close)
On Balance Volume (Obv)
Volume Indicators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
- Volume
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function OBV(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = OBV(ta[price].values, ta["Volume"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
PLUS_DI(ta::TimeSeries.TimeArray; time_period=Integer(14), price=:Close)
Plus Directional Indicator (PlusDI)
Momentum Indicators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- High
- Low
- Close
OptionalInputArguments:
- time_period=Integer(14)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function PLUS_DI(ta::TimeSeries.TimeArray; time_period=Integer(14), price=:Close)
price = string(price)
result = PLUS_DI(ta["High"].values, ta["Low"].values, ta["Close"].values, time_period=time_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
PLUS_DM(ta::TimeSeries.TimeArray; time_period=Integer(14), price=:Close)
Plus Directional Movement (PlusDM)
Momentum Indicators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- High
- Low
OptionalInputArguments:
- time_period=Integer(14)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function PLUS_DM(ta::TimeSeries.TimeArray; time_period=Integer(14), price=:Close)
price = string(price)
result = PLUS_DM(ta["High"].values, ta["Low"].values, time_period=time_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
PPO(ta::TimeSeries.TimeArray; fast_period=Integer(12), slow_period=Integer(26), ma_type=TA_MAType(0), price=:Close)
Percentage Price Oscillator (Ppo)
Momentum Indicators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
- fast_period=Integer(12)
- slow_period=Integer(26)
- ma_type=TA_MAType(0)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function PPO(ta::TimeSeries.TimeArray; fast_period=Integer(12), slow_period=Integer(26), ma_type=TA_MAType(0), price=:Close)
price = string(price)
result = PPO(ta[price].values, fast_period=fast_period, slow_period=slow_period, ma_type=ma_type)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
ROC(ta::TimeSeries.TimeArray; time_period=Integer(10), price=:Close)
Rate of change : ((price/prevPrice)-1)*100 (Roc)
Momentum Indicators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
- time_period=Integer(10)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function ROC(ta::TimeSeries.TimeArray; time_period=Integer(10), price=:Close)
price = string(price)
result = ROC(ta[price].values, time_period=time_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
ROCP(ta::TimeSeries.TimeArray; time_period=Integer(10), price=:Close)
Rate of change Percentage: (price-prevPrice)/prevPrice (RocP)
Momentum Indicators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
- time_period=Integer(10)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function ROCP(ta::TimeSeries.TimeArray; time_period=Integer(10), price=:Close)
price = string(price)
result = ROCP(ta[price].values, time_period=time_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
ROCR(ta::TimeSeries.TimeArray; time_period=Integer(10), price=:Close)
Rate of change ratio: (price/prevPrice) (RocR)
Momentum Indicators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
- time_period=Integer(10)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function ROCR(ta::TimeSeries.TimeArray; time_period=Integer(10), price=:Close)
price = string(price)
result = ROCR(ta[price].values, time_period=time_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
ROCR100(ta::TimeSeries.TimeArray; time_period=Integer(10), price=:Close)
Rate of change ratio 100 scale: (price/prevPrice)*100 (RocR100)
Momentum Indicators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
- time_period=Integer(10)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function ROCR100(ta::TimeSeries.TimeArray; time_period=Integer(10), price=:Close)
price = string(price)
result = ROCR100(ta[price].values, time_period=time_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
RSI(ta::TimeSeries.TimeArray; time_period=Integer(14), price=:Close)
Relative Strength Index (Rsi)
Momentum Indicators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
- time_period=Integer(14)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function RSI(ta::TimeSeries.TimeArray; time_period=Integer(14), price=:Close)
price = string(price)
result = RSI(ta[price].values, time_period=time_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
SAR(ta::TimeSeries.TimeArray; acceleration_factor=AbstractFloat(2.000000e-2), af_maximum=AbstractFloat(2.000000e-1), price=:Close)
Parabolic SAR (Sar)
Overlap Studies
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- High
- Low
OptionalInputArguments:
- acceleration_factor=AbstractFloat(2.000000e-2)
- af_maximum=AbstractFloat(2.000000e-1)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function SAR(ta::TimeSeries.TimeArray; acceleration_factor=AbstractFloat(2.000000e-2), af_maximum=AbstractFloat(2.000000e-1), price=:Close)
price = string(price)
result = SAR(ta["High"].values, ta["Low"].values, acceleration_factor=acceleration_factor, af_maximum=af_maximum)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
SAREXT(ta::TimeSeries.TimeArray; start_value=AbstractFloat(0.000000e+0), offset_on_reverse=AbstractFloat(0.000000e+0), af_init_long=AbstractFloat(2.000000e-2), af_long=AbstractFloat(2.000000e-2), af_max_long=AbstractFloat(2.000000e-1), af_init_short=AbstractFloat(2.000000e-2), af_short=AbstractFloat(2.000000e-2), af_max_short=AbstractFloat(2.000000e-1), price=:Close)
Parabolic SAR - Extended (SarExt)
Overlap Studies
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- High
- Low
OptionalInputArguments:
- start_value=AbstractFloat(0.000000e+0)
- offset_on_reverse=AbstractFloat(0.000000e+0)
- af_init_long=AbstractFloat(2.000000e-2)
- af_long=AbstractFloat(2.000000e-2)
- af_max_long=AbstractFloat(2.000000e-1)
- af_init_short=AbstractFloat(2.000000e-2)
- af_short=AbstractFloat(2.000000e-2)
- af_max_short=AbstractFloat(2.000000e-1)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function SAREXT(ta::TimeSeries.TimeArray; start_value=AbstractFloat(0.000000e+0), offset_on_reverse=AbstractFloat(0.000000e+0), af_init_long=AbstractFloat(2.000000e-2), af_long=AbstractFloat(2.000000e-2), af_max_long=AbstractFloat(2.000000e-1), af_init_short=AbstractFloat(2.000000e-2), af_short=AbstractFloat(2.000000e-2), af_max_short=AbstractFloat(2.000000e-1), price=:Close)
price = string(price)
result = SAREXT(ta["High"].values, ta["Low"].values, start_value=start_value, offset_on_reverse=offset_on_reverse, af_init_long=af_init_long, af_long=af_long, af_max_long=af_max_long, af_init_short=af_init_short, af_short=af_short, af_max_short=af_max_short)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
SIN(ta::TimeSeries.TimeArray, price=:Close)
Vector Trigonometric Sin (Sin)
Math Transform
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function SIN(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = SIN(ta[price].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
SINH(ta::TimeSeries.TimeArray, price=:Close)
Vector Trigonometric Sinh (Sinh)
Math Transform
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function SINH(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = SINH(ta[price].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
SMA(ta::TimeSeries.TimeArray; time_period=Integer(30), price=:Close)
Simple Moving Average (Sma)
Overlap Studies
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
- time_period=Integer(30)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function SMA(ta::TimeSeries.TimeArray; time_period=Integer(30), price=:Close)
price = string(price)
result = SMA(ta[price].values, time_period=time_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
SQRT(ta::TimeSeries.TimeArray, price=:Close)
Vector Square Root (Sqrt)
Math Transform
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function SQRT(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = SQRT(ta[price].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
STDDEV(ta::TimeSeries.TimeArray; time_period=Integer(5), deviations=AbstractFloat(1.000000e+0), price=:Close)
Standard Deviation (StdDev)
Statistic Functions
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
- time_period=Integer(5)
- deviations=AbstractFloat(1.000000e+0)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function STDDEV(ta::TimeSeries.TimeArray; time_period=Integer(5), deviations=AbstractFloat(1.000000e+0), price=:Close)
price = string(price)
result = STDDEV(ta[price].values, time_period=time_period, deviations=deviations)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
STOCH(ta::TimeSeries.TimeArray; fast_k_period=Integer(5), slow_k_period=Integer(3), slow_k_ma=TA_MAType(0), slow_d_period=Integer(3), slow_d_ma=TA_MAType(0), price=:Close)
Stochastic (Stoch)
Momentum Indicators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- High
- Low
- Close
OptionalInputArguments:
- fast_k_period=Integer(5)
- slow_k_period=Integer(3)
- slow_k_ma=TA_MAType(0)
- slow_d_period=Integer(3)
- slow_d_ma=TA_MAType(0)
Returns:
- TimeSeries.TimeArray with:
- outSlowK
- outSlowD
"""
function STOCH(ta::TimeSeries.TimeArray; fast_k_period=Integer(5), slow_k_period=Integer(3), slow_k_ma=TA_MAType(0), slow_d_period=Integer(3), slow_d_ma=TA_MAType(0), price=:Close)
price = string(price)
result = STOCH(ta["High"].values, ta["Low"].values, ta["Close"].values, fast_k_period=fast_k_period, slow_k_period=slow_k_period, slow_k_ma=slow_k_ma, slow_d_period=slow_d_period, slow_d_ma=slow_d_ma)
dt = ta.timestamp
out = TimeArray(dt, result, String["outSlowK", "outSlowD"])
out
end
"""
STOCHF(ta::TimeSeries.TimeArray; fast_k_period=Integer(5), fast_d_period=Integer(3), fast_d_ma=TA_MAType(0), price=:Close)
Stochastic Fast (StochF)
Momentum Indicators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- High
- Low
- Close
OptionalInputArguments:
- fast_k_period=Integer(5)
- fast_d_period=Integer(3)
- fast_d_ma=TA_MAType(0)
Returns:
- TimeSeries.TimeArray with:
- outFastK
- outFastD
"""
function STOCHF(ta::TimeSeries.TimeArray; fast_k_period=Integer(5), fast_d_period=Integer(3), fast_d_ma=TA_MAType(0), price=:Close)
price = string(price)
result = STOCHF(ta["High"].values, ta["Low"].values, ta["Close"].values, fast_k_period=fast_k_period, fast_d_period=fast_d_period, fast_d_ma=fast_d_ma)
dt = ta.timestamp
out = TimeArray(dt, result, String["outFastK", "outFastD"])
out
end
"""
STOCHRSI(ta::TimeSeries.TimeArray; time_period=Integer(14), fast_k_period=Integer(5), fast_d_period=Integer(3), fast_d_ma=TA_MAType(0), price=:Close)
Stochastic Relative Strength Index (StochRsi)
Momentum Indicators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
- time_period=Integer(14)
- fast_k_period=Integer(5)
- fast_d_period=Integer(3)
- fast_d_ma=TA_MAType(0)
Returns:
- TimeSeries.TimeArray with:
- outFastK
- outFastD
"""
function STOCHRSI(ta::TimeSeries.TimeArray; time_period=Integer(14), fast_k_period=Integer(5), fast_d_period=Integer(3), fast_d_ma=TA_MAType(0), price=:Close)
price = string(price)
result = STOCHRSI(ta[price].values, time_period=time_period, fast_k_period=fast_k_period, fast_d_period=fast_d_period, fast_d_ma=fast_d_ma)
dt = ta.timestamp
out = TimeArray(dt, result, String["outFastK", "outFastD"])
out
end
"""
SUB(ta::TimeSeries.TimeArray, ta2::TimeSeries.TimeArray, price=:Close)
Vector Arithmetic Substraction (Sub)
Math Operators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
- ta2::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function SUB(ta::TimeSeries.TimeArray, ta2::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = SUB(ta[price].values, ta2[price].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
SUM(ta::TimeSeries.TimeArray; time_period=Integer(30), price=:Close)
Summation (Sum)
Math Operators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
- time_period=Integer(30)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function SUM(ta::TimeSeries.TimeArray; time_period=Integer(30), price=:Close)
price = string(price)
result = SUM(ta[price].values, time_period=time_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
T3(ta::TimeSeries.TimeArray; time_period=Integer(5), volume_factor=AbstractFloat(7.000000e-1), price=:Close)
Triple Exponential Moving Average (T3) (T3)
Overlap Studies
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
- time_period=Integer(5)
- volume_factor=AbstractFloat(7.000000e-1)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function T3(ta::TimeSeries.TimeArray; time_period=Integer(5), volume_factor=AbstractFloat(7.000000e-1), price=:Close)
price = string(price)
result = T3(ta[price].values, time_period=time_period, volume_factor=volume_factor)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
TAN(ta::TimeSeries.TimeArray, price=:Close)
Vector Trigonometric Tan (Tan)
Math Transform
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function TAN(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = TAN(ta[price].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
TANH(ta::TimeSeries.TimeArray, price=:Close)
Vector Trigonometric Tanh (Tanh)
Math Transform
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function TANH(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = TANH(ta[price].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
TEMA(ta::TimeSeries.TimeArray; time_period=Integer(30), price=:Close)
Triple Exponential Moving Average (Tema)
Overlap Studies
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
- time_period=Integer(30)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function TEMA(ta::TimeSeries.TimeArray; time_period=Integer(30), price=:Close)
price = string(price)
result = TEMA(ta[price].values, time_period=time_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
TRANGE(ta::TimeSeries.TimeArray, price=:Close)
True Range (TrueRange)
Volatility Indicators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function TRANGE(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = TRANGE(ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
TRIMA(ta::TimeSeries.TimeArray; time_period=Integer(30), price=:Close)
Triangular Moving Average (Trima)
Overlap Studies
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
- time_period=Integer(30)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function TRIMA(ta::TimeSeries.TimeArray; time_period=Integer(30), price=:Close)
price = string(price)
result = TRIMA(ta[price].values, time_period=time_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
TRIX(ta::TimeSeries.TimeArray; time_period=Integer(30), price=:Close)
1-day Rate-Of-Change (ROC) of a Triple Smooth EMA (Trix)
Momentum Indicators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
- time_period=Integer(30)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function TRIX(ta::TimeSeries.TimeArray; time_period=Integer(30), price=:Close)
price = string(price)
result = TRIX(ta[price].values, time_period=time_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
TSF(ta::TimeSeries.TimeArray; time_period=Integer(14), price=:Close)
Time Series Forecast (Tsf)
Statistic Functions
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
- time_period=Integer(14)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function TSF(ta::TimeSeries.TimeArray; time_period=Integer(14), price=:Close)
price = string(price)
result = TSF(ta[price].values, time_period=time_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
TYPPRICE(ta::TimeSeries.TimeArray, price=:Close)
Typical Price (TypPrice)
Price Transform
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function TYPPRICE(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = TYPPRICE(ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
ULTOSC(ta::TimeSeries.TimeArray; first_period=Integer(7), second_period=Integer(14), third_period=Integer(28), price=:Close)
Ultimate Oscillator (UltOsc)
Momentum Indicators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- High
- Low
- Close
OptionalInputArguments:
- first_period=Integer(7)
- second_period=Integer(14)
- third_period=Integer(28)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function ULTOSC(ta::TimeSeries.TimeArray; first_period=Integer(7), second_period=Integer(14), third_period=Integer(28), price=:Close)
price = string(price)
result = ULTOSC(ta["High"].values, ta["Low"].values, ta["Close"].values, first_period=first_period, second_period=second_period, third_period=third_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
VAR(ta::TimeSeries.TimeArray; time_period=Integer(5), deviations=AbstractFloat(1.000000e+0), price=:Close)
Variance (Variance)
Statistic Functions
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
- time_period=Integer(5)
- deviations=AbstractFloat(1.000000e+0)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function VAR(ta::TimeSeries.TimeArray; time_period=Integer(5), deviations=AbstractFloat(1.000000e+0), price=:Close)
price = string(price)
result = VAR(ta[price].values, time_period=time_period, deviations=deviations)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
WCLPRICE(ta::TimeSeries.TimeArray, price=:Close)
Weighted Close Price (WclPrice)
Price Transform
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- High
- Low
- Close
OptionalInputArguments:
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function WCLPRICE(ta::TimeSeries.TimeArray, price=:Close)
price = string(price)
result = WCLPRICE(ta["High"].values, ta["Low"].values, ta["Close"].values, )
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
WILLR(ta::TimeSeries.TimeArray; time_period=Integer(14), price=:Close)
Williams' %R (WillR)
Momentum Indicators
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- High
- Low
- Close
OptionalInputArguments:
- time_period=Integer(14)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function WILLR(ta::TimeSeries.TimeArray; time_period=Integer(14), price=:Close)
price = string(price)
result = WILLR(ta["High"].values, ta["Low"].values, ta["Close"].values, time_period=time_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
"""
WMA(ta::TimeSeries.TimeArray; time_period=Integer(30), price=:Close)
Weighted Moving Average (Wma)
Overlap Studies
Level: 2 - DataFrame
Arguments:
RequiredInputArguments:
- ta::TimeSeries.TimeArray with:
- price
OptionalInputArguments:
- time_period=Integer(30)
Returns:
- TimeSeries.TimeArray with:
- Real
"""
function WMA(ta::TimeSeries.TimeArray; time_period=Integer(30), price=:Close)
price = string(price)
result = WMA(ta[price].values, time_period=time_period)
dt = ta.timestamp
out = TimeArray(dt, result, String["Real"])
out
end
# end of auto generated file