QuantConnect Server Applications
d4ca27f93f
Co-authored-by: Python Stubs Deployer <stubs-deploy@quantconnect.com>
332 lines
11 KiB
Python
332 lines
11 KiB
Python
from .__Statistics_1 import *
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import typing
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import System.Collections.Generic
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import System
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import QuantConnect.Statistics
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import QuantConnect.Orders
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import QuantConnect.Interfaces
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import QuantConnect
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import datetime
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# no functions
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# classes
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class AlgorithmPerformance(System.object):
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"""
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The QuantConnect.Statistics.AlgorithmPerformance class is a wrapper for QuantConnect.Statistics.AlgorithmPerformance.TradeStatistics and QuantConnect.Statistics.AlgorithmPerformance.PortfolioStatistics
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AlgorithmPerformance(trades: List[Trade], profitLoss: SortedDictionary[DateTime, Decimal], equity: SortedDictionary[DateTime, Decimal], listPerformance: List[float], listBenchmark: List[float], startingCapital: Decimal)
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AlgorithmPerformance()
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"""
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@typing.overload
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def __init__(self, trades: typing.List[QuantConnect.Statistics.Trade], profitLoss: System.Collections.Generic.SortedDictionary[datetime.datetime, float], equity: System.Collections.Generic.SortedDictionary[datetime.datetime, float], listPerformance: typing.List[float], listBenchmark: typing.List[float], startingCapital: float) -> QuantConnect.Statistics.AlgorithmPerformance:
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pass
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@typing.overload
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def __init__(self) -> QuantConnect.Statistics.AlgorithmPerformance:
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pass
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def __init__(self, *args) -> QuantConnect.Statistics.AlgorithmPerformance:
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pass
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ClosedTrades: typing.List[QuantConnect.Statistics.Trade]
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PortfolioStatistics: QuantConnect.Statistics.PortfolioStatistics
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TradeStatistics: QuantConnect.Statistics.TradeStatistics
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class FillGroupingMethod(System.Enum, System.IConvertible, System.IFormattable, System.IComparable):
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"""
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The method used to group order fills into trades
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enum FillGroupingMethod, values: FillToFill (0), FlatToFlat (1), FlatToReduced (2)
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"""
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value__: int
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FillToFill: 'FillGroupingMethod'
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FlatToFlat: 'FillGroupingMethod'
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FlatToReduced: 'FillGroupingMethod'
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class FillMatchingMethod(System.Enum, System.IConvertible, System.IFormattable, System.IComparable):
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"""
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The method used to match offsetting order fills
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enum FillMatchingMethod, values: FIFO (0), LIFO (1)
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"""
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value__: int
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FIFO: 'FillMatchingMethod'
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LIFO: 'FillMatchingMethod'
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class FitnessScoreManager(System.object):
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"""
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Implements a fitness score calculator needed to account for strategy volatility,
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returns, drawdown, and factor in the turnover to ensure the algorithm engagement
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is statistically significant
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FitnessScoreManager()
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"""
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def Initialize(self, algorithm: QuantConnect.Interfaces.IAlgorithm) -> None:
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pass
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@staticmethod
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def SigmoidalScale(valueToScale: float) -> float:
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pass
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def UpdateScores(self) -> None:
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pass
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FitnessScore: float
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PortfolioTurnover: float
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ReturnOverMaxDrawdown: float
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SortinoRatio: float
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class KellyCriterionManager(System.object):
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"""
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Class in charge of calculating the Kelly Criterion values.
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Will use the sample values of the last year.
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KellyCriterionManager()
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"""
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def AddNewValue(self, newValue: float, time: datetime.datetime) -> None:
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pass
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def UpdateScores(self) -> None:
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pass
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KellyCriterionEstimate: float
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KellyCriterionProbabilityValue: float
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class PortfolioStatistics(System.object):
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"""
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The QuantConnect.Statistics.PortfolioStatistics class represents a set of statistics calculated from equity and benchmark samples
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PortfolioStatistics(profitLoss: SortedDictionary[DateTime, Decimal], equity: SortedDictionary[DateTime, Decimal], listPerformance: List[float], listBenchmark: List[float], startingCapital: Decimal, tradingDaysPerYear: int)
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PortfolioStatistics()
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"""
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@staticmethod
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def GetRiskFreeRate() -> float:
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pass
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@typing.overload
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def __init__(self, profitLoss: System.Collections.Generic.SortedDictionary[datetime.datetime, float], equity: System.Collections.Generic.SortedDictionary[datetime.datetime, float], listPerformance: typing.List[float], listBenchmark: typing.List[float], startingCapital: float, tradingDaysPerYear: int) -> QuantConnect.Statistics.PortfolioStatistics:
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pass
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@typing.overload
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def __init__(self) -> QuantConnect.Statistics.PortfolioStatistics:
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pass
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def __init__(self, *args) -> QuantConnect.Statistics.PortfolioStatistics:
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pass
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Alpha: float
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AnnualStandardDeviation: float
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AnnualVariance: float
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AverageLossRate: float
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AverageWinRate: float
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Beta: float
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CompoundingAnnualReturn: float
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Drawdown: float
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Expectancy: float
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InformationRatio: float
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LossRate: float
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ProbabilisticSharpeRatio: float
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ProfitLossRatio: float
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SharpeRatio: float
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TotalNetProfit: float
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TrackingError: float
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TreynorRatio: float
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WinRate: float
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class Statistics(System.object):
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"""
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Calculate all the statistics required from the backtest, based on the equity curve and the profit loss statement.
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Statistics()
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"""
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@staticmethod
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def Alpha(algoPerformance: typing.List[float], benchmarkPerformance: typing.List[float], riskFreeRate: float) -> float:
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pass
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@staticmethod
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def AnnualPerformance(performance: typing.List[float], tradingDaysPerYear: float) -> float:
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pass
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@staticmethod
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def AnnualStandardDeviation(performance: typing.List[float], tradingDaysPerYear: float) -> float:
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pass
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@staticmethod
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def AnnualVariance(performance: typing.List[float], tradingDaysPerYear: float) -> float:
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pass
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@staticmethod
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def Beta(algoPerformance: typing.List[float], benchmarkPerformance: typing.List[float]) -> float:
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pass
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@staticmethod
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def CompoundingAnnualPerformance(startingCapital: float, finalCapital: float, years: float) -> float:
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pass
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@staticmethod
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def DrawdownPercent(equityOverTime: System.Collections.Generic.SortedDictionary[datetime.datetime, float], rounding: int) -> float:
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pass
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@staticmethod
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def DrawdownValue(equityOverTime: System.Collections.Generic.SortedDictionary[datetime.datetime, float], rounding: int) -> float:
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pass
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@staticmethod
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def Generate(pointsEquity: typing.List[QuantConnect.ChartPoint], profitLoss: System.Collections.Generic.SortedDictionary[datetime.datetime, float], pointsPerformance: typing.List[QuantConnect.ChartPoint], unsortedBenchmark: System.Collections.Generic.Dictionary[datetime.datetime, float], startingCash: float, totalFees: float, totalTrades: float, tradingDaysPerYear: float) -> System.Collections.Generic.Dictionary[str, str]:
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pass
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@staticmethod
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def InformationRatio(algoPerformance: typing.List[float], benchmarkPerformance: typing.List[float]) -> float:
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pass
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@staticmethod
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def ObservedSharpeRatio(listPerformance: typing.List[float]) -> float:
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pass
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@staticmethod
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def ProbabilisticSharpeRatio(listPerformance: typing.List[float], benchmarkSharpeRatio: float) -> float:
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pass
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@staticmethod
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def ProfitLossRatio(averageWin: float, averageLoss: float) -> float:
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pass
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@staticmethod
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def SharpeRatio(algoPerformance: typing.List[float], riskFreeRate: float) -> float:
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pass
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@staticmethod
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def TrackingError(algoPerformance: typing.List[float], benchmarkPerformance: typing.List[float], tradingDaysPerYear: float) -> float:
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pass
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@staticmethod
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def TreynorRatio(algoPerformance: typing.List[float], benchmarkPerformance: typing.List[float], riskFreeRate: float) -> float:
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pass
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class StatisticsBuilder(System.object):
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""" The QuantConnect.Statistics.StatisticsBuilder class creates summary and rolling statistics from trades, equity and benchmark points """
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@staticmethod
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def Generate(trades: typing.List[QuantConnect.Statistics.Trade], profitLoss: System.Collections.Generic.SortedDictionary[datetime.datetime, float], pointsEquity: typing.List[QuantConnect.ChartPoint], pointsPerformance: typing.List[QuantConnect.ChartPoint], pointsBenchmark: typing.List[QuantConnect.ChartPoint], startingCapital: float, totalFees: float, totalTransactions: int) -> QuantConnect.Statistics.StatisticsResults:
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pass
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__all__: list
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class StatisticsResults(System.object):
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"""
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The QuantConnect.Statistics.StatisticsResults class represents total and rolling statistics for an algorithm
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StatisticsResults(totalPerformance: AlgorithmPerformance, rollingPerformances: Dictionary[str, AlgorithmPerformance], summary: Dictionary[str, str])
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StatisticsResults()
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"""
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@typing.overload
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def __init__(self, totalPerformance: QuantConnect.Statistics.AlgorithmPerformance, rollingPerformances: System.Collections.Generic.Dictionary[str, QuantConnect.Statistics.AlgorithmPerformance], summary: System.Collections.Generic.Dictionary[str, str]) -> QuantConnect.Statistics.StatisticsResults:
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pass
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@typing.overload
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def __init__(self) -> QuantConnect.Statistics.StatisticsResults:
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pass
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def __init__(self, *args) -> QuantConnect.Statistics.StatisticsResults:
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pass
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RollingPerformances: System.Collections.Generic.Dictionary[str, QuantConnect.Statistics.AlgorithmPerformance]
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Summary: System.Collections.Generic.Dictionary[str, str]
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TotalPerformance: QuantConnect.Statistics.AlgorithmPerformance
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class Trade(System.object):
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"""
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Represents a closed trade
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Trade()
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"""
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Direction: QuantConnect.Statistics.TradeDirection
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Duration: datetime.timedelta
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EndTradeDrawdown: float
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EntryPrice: float
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EntryTime: datetime.datetime
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ExitPrice: float
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ExitTime: datetime.datetime
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MAE: float
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MFE: float
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ProfitLoss: float
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Quantity: float
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Symbol: QuantConnect.Symbol
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TotalFees: float
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class TradeBuilder(System.object, QuantConnect.Interfaces.ITradeBuilder):
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"""
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The QuantConnect.Statistics.TradeBuilder class generates trades from executions and market price updates
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TradeBuilder(groupingMethod: FillGroupingMethod, matchingMethod: FillMatchingMethod)
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"""
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def HasOpenPosition(self, symbol: QuantConnect.Symbol) -> bool:
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pass
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def ProcessFill(self, fill: QuantConnect.Orders.OrderEvent, conversionRate: float, feeInAccountCurrency: float, multiplier: float) -> None:
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pass
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def SetLiveMode(self, live: bool) -> None:
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pass
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def SetMarketPrice(self, symbol: QuantConnect.Symbol, price: float) -> None:
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pass
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def __init__(self, groupingMethod: QuantConnect.Statistics.FillGroupingMethod, matchingMethod: QuantConnect.Statistics.FillMatchingMethod) -> QuantConnect.Statistics.TradeBuilder:
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pass
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ClosedTrades: typing.List[QuantConnect.Statistics.Trade]
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