Python ValueAlphaModel Examples

Example #1

File: main.py Project: ajmal017/Ollie-Hooper-Trading-Bot-2

 def Initialize(self):
     self.SetStartDate(2003, 1, 1)
     self.SetCash(100000)
     
     # Data resolution
     self.UniverseSettings.Resolution = Resolution.Minute
     
     # Universe selection model
     self.securities = []
     self.CustomUniverseSelectionModel = FactorUniverseSelectionModel(self)
     self.AddUniverse(self.CustomUniverseSelectionModel.SelectCoarse, self.CustomUniverseSelectionModel.SelectFine)
     
     # Alpha model
     self.CustomAlphaModel = ValueAlphaModel()
     
     # Portfolio construction model
     self.CustomPortfolioConstructionModel = OptimisationPortfolioConstructionModel()
     
     # Execution model
     self.CustomExecution = Execution()
     
     # Add SPY for trading days data
     self.AddEquity('SPY', Resolution.Daily)
     
     # Schedule rebalancing
     self.Schedule.On(self.DateRules.EveryDay('SPY'), self.TimeRules.At(13, 0), Action(self.RebalancePortfolio))
     
     # Init charting
     InitCharts(self)
     
     # Schedule charting
     self.Schedule.On(self.DateRules.Every(DayOfWeek.Friday), self.TimeRules.BeforeMarketClose('SPY', 0), Action(self.PlotCharts))

Example #2

class TradingBot(QCAlgorithm):
    def Initialize(self):
        self.SetStartDate(2020, 1, 1)
        self.SetEndDate(datetime.now() - timedelta(10))
        self.SetCash(100000)

        # *Data Resolution
        self.UniverseSettings.Resolution = Resolution.Minute

        # *Universe selection model; runs with the above data resolution
        # custom universe selection model class created -- from universe_selection -->UniverseSelectionModel()
        self.securities = []
        self.CustomUniverseSelectionModel = FactorUniverseSelectionModel(self)
        self.AddUniverse(self.CustomUniverseSelectionModel.SelectCoarse,
                         self.CustomUniverseSelectionModel.SelectFine)

        # *Alpha model; A
        self.CustomAlphaModel = ValueAlphaModel()

        # *Portfolio construction model; B
        self.CustomPortfolioConstructionModel = OptimisationPortfolioConstructionModel(
            turnover=0.05, max_wt=0.05, longshort=True)

        #Eexecution model; C
        self.CustomExecution = Execution(liq_tol=0.005)

        # *Add SPY for trading days data; a
        self.AddEquity('SPY', Resolution.Daily)

        # *Scheduling rebalancing; b ; we take the a daily resloution and at 2 oclock we execute a rebalance
        self.Schedule.On(self.DateRules.EveryDay('SPY'),
                         self.TimeRules.At(13, 0),
                         Action(self.RebalancePortfolio))

        # Init charting
        InitCharts(self)

        # Schedule charting
        self.Schedule.On(self.DateRules.Every(DayOfWeek.Friday),
                         self.TimeRules.BeforeMarketClose('SPY', 0),
                         Action(self.PlotCharts))

    def OnData(self, data):
        pass

    # this controls the A B C ; we chose when we rebalance; we generate our alpha scores ;we pass the alpha scores into our portfolio construction
    #next we execute orders based on our portfolio construction
    def RebalancePortfolio(self):
        alpha_df = self.CustomAlphaModel.GenerateAlphaScores(
            self, self.securities)
        portfolio = self.CustomPortfolioConstructionModel.GenerateOptimalPortfolio(
            self, alpha_df)
        self.CustomExecution.ExecutePortfolio(self, portfolio)

    def PlotCharts(self):
        PlotPerformanceChart(self)
        PlotPosConcentrationChart(self)
        PlotStockCountChart(self)
        PlotExposureChart(self)

Example #3

class TradingBot(QCAlgorithm):
    def Initialize(self):
        self.SetStartDate(2019, 1, 1)
        self.SetEndDate(2020, 1, 1)
        self.SetCash(100000)

        # Data resolution
        # By default, assets selected by universe selection are requested with minute resolution data.
        # https://www.quantconnect.com/docs/algorithm-reference/universes
        self.UniverseSettings.Resolution = Resolution.Minute
        self.UniverseSettings.Leverage = 1

        # Universe selection model
        self.securities = []
        self.CustomUniverseSelectionModel = FactorUniverseSelectionModel(self)
        self.AddUniverse(self.CustomUniverseSelectionModel.SelectCoarse,
                         self.CustomUniverseSelectionModel.SelectFine)

        # Alpha model
        self.CustomAlphaModel = ValueAlphaModel()

        # Portfolio construction model
        self.CustomPortfolioConstructionModel = OptimisationPortfolioConstructionModel(
            turnover=0.05, max_wt=0.05, longshort=True)

        # Execution model
        self.CustomExecution = Execution(liq_tol=0.005)

        # Add SPY for trading days data
        self.AddEquity('SPY', Resolution.Daily)

        # Schedule rebalancing
        self.Schedule.On(self.DateRules.EveryDay('SPY'),
                         self.TimeRules.At(13, 0),
                         Action(self.RebalancePortfolio))

        # Init charting
        InitCharts(self)

        # Schedule charting
        self.Schedule.On(self.DateRules.Every(DayOfWeek.Friday),
                         self.TimeRules.BeforeMarketClose('SPY', 0),
                         Action(self.PlotCharts))

    def OnData(self, data):
        pass

    def RebalancePortfolio(self):
        alpha_df = self.CustomAlphaModel.GenerateAlphaScores(
            self, self.securities)
        portfolio = self.CustomPortfolioConstructionModel.GenerateOptimalPortfolio(
            self, alpha_df)
        self.CustomExecution.ExecutePortfolio(self, portfolio)

    def PlotCharts(self):
        PlotPerformanceChart(self)
        PlotPosConcentrationChart(self)
        PlotStockCountChart(self)
        PlotExposureChart(self)

Example #4

File: main.py Project: Jonahbaka/Ollie-Hooper-Trading-Bot-2

class TradingBot(QCAlgorithm):
    def Initialize(self):
        self.SetStartDate(2003, 1, 1)
        self.SetCash(100000)

        # Data resolution
        self.UniverseSettings.Resolution = Resolution.Minute

        # Universe selection model
        self.securities = []
        self.CustomUniverseSelectionModel = FactorUniverseSelectionModel(self)
        self.AddUniverse(self.CustomUniverseSelectionModel.SelectCoarse,
                         self.CustomUniverseSelectionModel.SelectFine)

        # Alpha model
        self.CustomAlphaModel = ValueAlphaModel()

        # Portfolio construction model
        self.CustomPortfolioConstructionModel = OptimisationPortfolioConstructionModel(
        )

        # Execution model
        self.CustomExecution = Execution()

        # Add SPY for trading days data
        self.AddEquity('SPY', Resolution.Daily)

        # Schedule rebalancing
        self.Schedule.On(self.DateRules.EveryDay('SPY'),
                         self.TimeRules.At(13, 0),
                         Action(self.RebalancePortfolio))

        # Init charting
        InitCharts(self)

        # Schedule charting
        self.Schedule.On(self.DateRules.Every(DayOfWeek.Friday),
                         self.TimeRules.BeforeMarketClose('SPY', 0),
                         Action(self.PlotCharts))

    def OnData(self, data):
        pass

    def RebalancePortfolio(self):
        alpha_df = self.CustomAlphaModel.GenerateAlphaScores(
            self, self.securities)
        portfolio = self.CustomPortfolioConstructionModel.GenerateOptimalPortfolio(
            self, alpha_df)
        self.CustomExecution.ExecutePortfolio(self, portfolio)

    def PlotCharts(self):
        PlotPerformanceChart(self)
        PlotPosConcentrationChart(self)

Example #5

    def Initialize(self):
        self.SetStartDate(2020, 1, 1)
        self.SetEndDate(datetime.now() - timedelta(10))
        self.SetCash(100000)

        # *Data Resolution
        self.UniverseSettings.Resolution = Resolution.Minute

        # *Universe selection model; runs with the above data resolution
        # custom universe selection model class created -- from universe_selection -->UniverseSelectionModel()
        self.securities = []
        self.CustomUniverseSelectionModel = FactorUniverseSelectionModel(self)
        self.AddUniverse(self.CustomUniverseSelectionModel.SelectCoarse,
                         self.CustomUniverseSelectionModel.SelectFine)

        # *Alpha model; A
        self.CustomAlphaModel = ValueAlphaModel()

        # *Portfolio construction model; B
        self.CustomPortfolioConstructionModel = OptimisationPortfolioConstructionModel(
            turnover=0.05, max_wt=0.05, longshort=True)

        #Eexecution model; C
        self.CustomExecution = Execution(liq_tol=0.005)

        # *Add SPY for trading days data; a
        self.AddEquity('SPY', Resolution.Daily)

        # *Scheduling rebalancing; b ; we take the a daily resloution and at 2 oclock we execute a rebalance
        self.Schedule.On(self.DateRules.EveryDay('SPY'),
                         self.TimeRules.At(13, 0),
                         Action(self.RebalancePortfolio))

        # Init charting
        InitCharts(self)

        # Schedule charting
        self.Schedule.On(self.DateRules.Every(DayOfWeek.Friday),
                         self.TimeRules.BeforeMarketClose('SPY', 0),
                         Action(self.PlotCharts))