QuantConnect Server Applications
d4ca27f93f
Co-authored-by: Python Stubs Deployer <stubs-deploy@quantconnect.com>
253 lines
10 KiB
Python
253 lines
10 KiB
Python
from .____init___7 import *
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import typing
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import System.IO
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import System.Collections.Generic
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import System
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import QuantConnect.Indicators
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import QuantConnect.Data.Market
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import QuantConnect.Data
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import QuantConnect
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import Python.Runtime
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import datetime
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class KaufmanAdaptiveMovingAverage(QuantConnect.Indicators.WindowIndicator[IndicatorDataPoint], QuantConnect.Indicators.IIndicatorWarmUpPeriodProvider, System.IComparable, QuantConnect.Indicators.IIndicator[IndicatorDataPoint], QuantConnect.Indicators.IIndicator, System.IComparable[IIndicator[IndicatorDataPoint]]):
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"""
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This indicator computes the Kaufman Adaptive Moving Average (KAMA).
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The Kaufman Adaptive Moving Average is calculated as explained here:
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http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:kaufman_s_adaptive_moving_average
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KaufmanAdaptiveMovingAverage(name: str, period: int, fastEmaPeriod: int, slowEmaPeriod: int)
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KaufmanAdaptiveMovingAverage(period: int, fastEmaPeriod: int, slowEmaPeriod: int)
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"""
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def Reset(self) -> None:
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pass
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@typing.overload
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def __init__(self, name: str, period: int, fastEmaPeriod: int, slowEmaPeriod: int) -> QuantConnect.Indicators.KaufmanAdaptiveMovingAverage:
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pass
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@typing.overload
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def __init__(self, period: int, fastEmaPeriod: int, slowEmaPeriod: int) -> QuantConnect.Indicators.KaufmanAdaptiveMovingAverage:
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pass
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def __init__(self, *args) -> QuantConnect.Indicators.KaufmanAdaptiveMovingAverage:
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pass
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IsReady: bool
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WarmUpPeriod: int
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class KeltnerChannels(QuantConnect.Indicators.BarIndicator, QuantConnect.Indicators.IIndicatorWarmUpPeriodProvider, System.IComparable, QuantConnect.Indicators.IIndicator[IBaseDataBar], QuantConnect.Indicators.IIndicator, System.IComparable[IIndicator[IBaseDataBar]]):
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"""
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This indicator creates a moving average (middle band) with an upper band and lower band
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fixed at k average true range multiples away from the middle band.
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KeltnerChannels(period: int, k: Decimal, movingAverageType: MovingAverageType)
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KeltnerChannels(name: str, period: int, k: Decimal, movingAverageType: MovingAverageType)
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"""
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def Reset(self) -> None:
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pass
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@typing.overload
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def __init__(self, period: int, k: float, movingAverageType: QuantConnect.Indicators.MovingAverageType) -> QuantConnect.Indicators.KeltnerChannels:
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pass
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@typing.overload
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def __init__(self, name: str, period: int, k: float, movingAverageType: QuantConnect.Indicators.MovingAverageType) -> QuantConnect.Indicators.KeltnerChannels:
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pass
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def __init__(self, *args) -> QuantConnect.Indicators.KeltnerChannels:
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pass
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AverageTrueRange: QuantConnect.Indicators.IndicatorBase[QuantConnect.Data.Market.IBaseDataBar]
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IsReady: bool
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LowerBand: QuantConnect.Indicators.IndicatorBase[QuantConnect.Data.Market.IBaseDataBar]
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MiddleBand: QuantConnect.Indicators.IndicatorBase[QuantConnect.Indicators.IndicatorDataPoint]
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UpperBand: QuantConnect.Indicators.IndicatorBase[QuantConnect.Data.Market.IBaseDataBar]
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WarmUpPeriod: int
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class LeastSquaresMovingAverage(QuantConnect.Indicators.WindowIndicator[IndicatorDataPoint], QuantConnect.Indicators.IIndicatorWarmUpPeriodProvider, System.IComparable, QuantConnect.Indicators.IIndicator[IndicatorDataPoint], QuantConnect.Indicators.IIndicator, System.IComparable[IIndicator[IndicatorDataPoint]]):
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"""
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The Least Squares Moving Average (LSMA) first calculates a least squares regression line
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over the preceding time periods, and then projects it forward to the current period. In
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essence, it calculates what the value would be if the regression line continued.
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Source: https://rtmath.net/helpFinAnalysis/html/b3fab79c-f4b2-40fb-8709-fdba43cdb363.htm
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LeastSquaresMovingAverage(name: str, period: int)
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LeastSquaresMovingAverage(period: int)
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"""
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def Reset(self) -> None:
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pass
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@typing.overload
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def __init__(self, name: str, period: int) -> QuantConnect.Indicators.LeastSquaresMovingAverage:
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pass
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@typing.overload
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def __init__(self, period: int) -> QuantConnect.Indicators.LeastSquaresMovingAverage:
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pass
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def __init__(self, *args) -> QuantConnect.Indicators.LeastSquaresMovingAverage:
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pass
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Intercept: QuantConnect.Indicators.IndicatorBase[QuantConnect.Indicators.IndicatorDataPoint]
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Slope: QuantConnect.Indicators.IndicatorBase[QuantConnect.Indicators.IndicatorDataPoint]
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WarmUpPeriod: int
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class LinearWeightedMovingAverage(QuantConnect.Indicators.WindowIndicator[IndicatorDataPoint], QuantConnect.Indicators.IIndicatorWarmUpPeriodProvider, System.IComparable, QuantConnect.Indicators.IIndicator[IndicatorDataPoint], QuantConnect.Indicators.IIndicator, System.IComparable[IIndicator[IndicatorDataPoint]]):
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"""
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Represents the traditional Weighted Moving Average indicator. The weight are linearly
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distributed according to the number of periods in the indicator.
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For example, a 4 period indicator will have a numerator of (4 * window[0]) + (3 * window[1]) + (2 * window[2]) + window[3]
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and a denominator of 4 + 3 + 2 + 1 = 10
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During the warm up period, IsReady will return false, but the LWMA will still be computed correctly because
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the denominator will be the minimum of Samples factorial or Size factorial and
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the computation iterates over that minimum value.
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The RollingWindow of inputs is created when the indicator is created.
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A RollingWindow of LWMAs is not saved. That is up to the caller.
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LinearWeightedMovingAverage(name: str, period: int)
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LinearWeightedMovingAverage(period: int)
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"""
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@typing.overload
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def __init__(self, name: str, period: int) -> QuantConnect.Indicators.LinearWeightedMovingAverage:
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pass
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@typing.overload
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def __init__(self, period: int) -> QuantConnect.Indicators.LinearWeightedMovingAverage:
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pass
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def __init__(self, *args) -> QuantConnect.Indicators.LinearWeightedMovingAverage:
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pass
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WarmUpPeriod: int
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class LogReturn(QuantConnect.Indicators.WindowIndicator[IndicatorDataPoint], QuantConnect.Indicators.IIndicatorWarmUpPeriodProvider, System.IComparable, QuantConnect.Indicators.IIndicator[IndicatorDataPoint], QuantConnect.Indicators.IIndicator, System.IComparable[IIndicator[IndicatorDataPoint]]):
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"""
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Represents the LogReturn indicator (LOGR)
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- log returns are useful for identifying price convergence/divergence in a given period
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- logr = log (current price / last price in period)
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LogReturn(name: str, period: int)
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LogReturn(period: int)
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"""
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@typing.overload
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def __init__(self, name: str, period: int) -> QuantConnect.Indicators.LogReturn:
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pass
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@typing.overload
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def __init__(self, period: int) -> QuantConnect.Indicators.LogReturn:
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pass
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def __init__(self, *args) -> QuantConnect.Indicators.LogReturn:
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pass
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WarmUpPeriod: int
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class MassIndex(QuantConnect.Indicators.TradeBarIndicator, QuantConnect.Indicators.IIndicatorWarmUpPeriodProvider, System.IComparable, QuantConnect.Indicators.IIndicator[TradeBar], QuantConnect.Indicators.IIndicator, System.IComparable[IIndicator[TradeBar]]):
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"""
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The Mass Index uses the high-low range to identify trend reversals based on range expansions.
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In this sense, the Mass Index is a volatility indicator that does not have a directional
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bias. Instead, the Mass Index identifies range bulges that can foreshadow a reversal of the
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current trend. Developed by Donald Dorsey.
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MassIndex(name: str, emaPeriod: int, sumPeriod: int)
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MassIndex(emaPeriod: int, sumPeriod: int)
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"""
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def Reset(self) -> None:
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pass
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@typing.overload
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def __init__(self, name: str, emaPeriod: int, sumPeriod: int) -> QuantConnect.Indicators.MassIndex:
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pass
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@typing.overload
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def __init__(self, emaPeriod: int, sumPeriod: int) -> QuantConnect.Indicators.MassIndex:
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pass
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def __init__(self, *args) -> QuantConnect.Indicators.MassIndex:
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pass
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IsReady: bool
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WarmUpPeriod: int
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class Maximum(QuantConnect.Indicators.WindowIndicator[IndicatorDataPoint], QuantConnect.Indicators.IIndicatorWarmUpPeriodProvider, System.IComparable, QuantConnect.Indicators.IIndicator[IndicatorDataPoint], QuantConnect.Indicators.IIndicator, System.IComparable[IIndicator[IndicatorDataPoint]]):
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"""
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Represents an indicator capable of tracking the maximum value and how many periods ago it occurred
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Maximum(period: int)
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Maximum(name: str, period: int)
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"""
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def Reset(self) -> None:
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pass
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@typing.overload
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def __init__(self, period: int) -> QuantConnect.Indicators.Maximum:
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pass
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@typing.overload
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def __init__(self, name: str, period: int) -> QuantConnect.Indicators.Maximum:
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pass
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def __init__(self, *args) -> QuantConnect.Indicators.Maximum:
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pass
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IsReady: bool
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PeriodsSinceMaximum: int
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WarmUpPeriod: int
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class MeanAbsoluteDeviation(QuantConnect.Indicators.WindowIndicator[IndicatorDataPoint], QuantConnect.Indicators.IIndicatorWarmUpPeriodProvider, System.IComparable, QuantConnect.Indicators.IIndicator[IndicatorDataPoint], QuantConnect.Indicators.IIndicator, System.IComparable[IIndicator[IndicatorDataPoint]]):
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"""
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This indicator computes the n-period mean absolute deviation.
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MeanAbsoluteDeviation(period: int)
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MeanAbsoluteDeviation(name: str, period: int)
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"""
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def Reset(self) -> None:
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pass
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@typing.overload
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def __init__(self, period: int) -> QuantConnect.Indicators.MeanAbsoluteDeviation:
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pass
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@typing.overload
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def __init__(self, name: str, period: int) -> QuantConnect.Indicators.MeanAbsoluteDeviation:
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pass
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def __init__(self, *args) -> QuantConnect.Indicators.MeanAbsoluteDeviation:
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pass
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IsReady: bool
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Mean: QuantConnect.Indicators.IndicatorBase[QuantConnect.Indicators.IndicatorDataPoint]
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WarmUpPeriod: int
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