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))
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)
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)
def Initialize(self): self.SetStartDate(2017, 1, 1) # Set Start Date self.SetEndDate(2020, 5, 20) self.SetCash(100000) # Set Strategy Cash # Weighting style - normalise or alpha_max (alpha maximisation w/ optimisation) self.weighting_style = 'normalise' # Market neutral self.mkt_neutral = True # Audio feature to use self.audio_feature = 'valence' # Get data self.data, self.etf_list, self.etf_country = self.DataSetup() # Add ETFs for etf in self.etf_list: self.AddEquity(etf, Resolution.Minute) # Portfolio construction model self.CustomPortfolioConstructionModel = OptimisationPortfolioConstructionModel( turnover=1, max_wt=0.2, longshort=True, mkt_neutral=self.mkt_neutral) # Execution model self.CustomExecution = Execution(liq_tol=0.005) # Schedule rebalancing self.Schedule.On(self.DateRules.Every(DayOfWeek.Wednesday), self.TimeRules.BeforeMarketClose('IVV', 210), Action(self.RebalancePortfolio)) # Init charting InitCharts(self) # Schedule charting self.Schedule.On(self.DateRules.Every(DayOfWeek.Wednesday), self.TimeRules.BeforeMarketClose('IVV', 5), Action(self.PlotCharts))
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)
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))
class StockifySentiment(QCAlgorithm): def Initialize(self): self.SetStartDate(2017, 1, 1) # Set Start Date self.SetEndDate(2020, 5, 20) self.SetCash(100000) # Set Strategy Cash # Weighting style - normalise or alpha_max (alpha maximisation w/ optimisation) self.weighting_style = 'normalise' # Market neutral self.mkt_neutral = True # Audio feature to use self.audio_feature = 'valence' # Get data self.data, self.etf_list, self.etf_country = self.DataSetup() # Add ETFs for etf in self.etf_list: self.AddEquity(etf, Resolution.Minute) # Portfolio construction model self.CustomPortfolioConstructionModel = OptimisationPortfolioConstructionModel( turnover=1, max_wt=0.2, longshort=True, mkt_neutral=self.mkt_neutral) # Execution model self.CustomExecution = Execution(liq_tol=0.005) # Schedule rebalancing self.Schedule.On(self.DateRules.Every(DayOfWeek.Wednesday), self.TimeRules.BeforeMarketClose('IVV', 210), Action(self.RebalancePortfolio)) # Init charting InitCharts(self) # Schedule charting self.Schedule.On(self.DateRules.Every(DayOfWeek.Wednesday), self.TimeRules.BeforeMarketClose('IVV', 5), Action(self.PlotCharts)) def OnData(self, data): pass def RebalancePortfolio(self): df = self.data.loc[self.Time - timedelta(7):self.Time].reset_index().set_index( 'symbol') if self.weighting_style == 'normalise': portfolio = normalise(df['alpha_score'], equal_ls=self.mkt_neutral) elif self.weighting_style == 'alpha_max': df = df[['alpha_score']] portfolio = self.CustomPortfolioConstructionModel.GenerateOptimalPortfolio( self, df) else: raise Exception('Invalid weighting style') self.CustomExecution.ExecutePortfolio(self, portfolio) def PlotCharts(self): PlotPerformanceChart(self) PlotExposureChart(self) PlotCountryExposureChart(self) def DataSetup(self): df = pd.read_csv( StringIO( self.Download( 'https://raw.githubusercontent.com/Ollie-Hooper/StockifySentiment/master/data/scores.csv' ))) data = df[['date', 'country', f's_{self.audio_feature}']].copy() data['date'] = pd.to_datetime(data['date']) data.rename(columns={f's_{self.audio_feature}': 'alpha_score'}, inplace=True) etf_df = pd.read_csv( StringIO( self.Download( 'https://raw.githubusercontent.com/Ollie-Hooper/StockifySentiment/master/data/etf.csv' ))) data = pd.merge(data, etf_df) data = data.sort_values('date') data.set_index(['date', 'symbol'], inplace=True) data = data[['alpha_score']] return data, etf_df['symbol'].to_list(), etf_df.set_index( 'symbol')['country_name'].to_dict()