def __init__(self, rebalancingParam=Resolution.Daily, lookback=1, period=63, resolution=Resolution.Daily, targetReturn=0.02, optimizer=None): """Initialize the model Args: rebalancingParam: Rebalancing parameter. If it is a timedelta, date rules or Resolution, it will be converted into a function. If None will be ignored. The function returns the next expected rebalance time for a given algorithm UTC DateTime. The function returns null if unknown, in which case the function will be called again in the next loop. Returning current time will trigger rebalance. lookback(int): Historical return lookback period period(int): The time interval of history price to calculate the weight resolution: The resolution of the history price optimizer(class): Method used to compute the portfolio weights""" self.lookback = lookback self.period = period self.resolution = resolution self.optimizer = MinimumVariancePortfolioOptimizer( -1, 1, targetReturn) if optimizer is None else optimizer self.symbolDataBySymbol = {} # If the argument is an instance of Resolution or Timedelta # Redefine rebalancingFunc rebalancingFunc = rebalancingParam if isinstance(rebalancingParam, int): rebalancingParam = Extensions.ToTimeSpan(rebalancingParam) if isinstance(rebalancingParam, timedelta): rebalancingFunc = lambda dt: dt + rebalancingParam if rebalancingFunc: self.SetRebalancingFunc(rebalancingFunc)
def __init__(self, rebalancingParam = None, lookback = 1, period = 63, resolution = Resolution.Daily, optimizer = None): """Initialize the model Args: lookback(int): Historical return lookback period period(int): The time interval of history price to calculate the weight resolution: The resolution of the history price optimizer(class): Method used to compute the portfolio weights""" self.lookback = lookback self.period = period self.resolution = resolution self.optimizer = MinimumVariancePortfolioOptimizer() if optimizer is None else optimizer self.symbolDataBySymbol = {} self.pendingRemoval = [] # If the argument is an instance of Resolution or Timedelta # Redefine rebalancingFunc rebalancingFunc = rebalancingParam if isinstance(rebalancingParam, int): rebalancingParam = Extensions.ToTimeSpan(rebalancingParam) if isinstance(rebalancingParam, timedelta): rebalancingFunc = lambda dt: dt + rebalancingParam if rebalancingFunc: self.SetRebalancingFunc(rebalancingFunc)
def __init__(self, lookback = 1, period = 63, resolution = Resolution.Daily, optimizer = None): """Initialize the model Args: lookback(int): Historical return lookback period period(int): The time interval of history price to calculate the weight resolution: The resolution of the history price optimizer(class): Method used to compute the portfolio weights""" self.lookback = lookback self.period = period self.resolution = resolution self.optimizer = MinimumVariancePortfolioOptimizer() if optimizer is None else optimizer self.symbolDataBySymbol = {} self.pendingRemoval = []
def __init__(self, rebalance = Resolution.Daily, portfolioBias = PortfolioBias.LongShort, lookback = 1, period = 63, resolution = Resolution.Daily, targetReturn = 0.02, optimizer = None): """Initialize the model Args: rebalance: Rebalancing parameter. If it is a timedelta, date rules or Resolution, it will be converted into a function. If None will be ignored. The function returns the next expected rebalance time for a given algorithm UTC DateTime. The function returns null if unknown, in which case the function will be called again in the next loop. Returning current time will trigger rebalance. portfolioBias: Specifies the bias of the portfolio (Short, Long/Short, Long) lookback(int): Historical return lookback period period(int): The time interval of history price to calculate the weight resolution: The resolution of the history price optimizer(class): Method used to compute the portfolio weights""" super().__init__() self.lookback = lookback self.period = period self.resolution = resolution self.portfolioBias = portfolioBias self.sign = lambda x: -1 if x < 0 else (1 if x > 0 else 0) lower = 0 if portfolioBias == PortfolioBias.Long else -1 upper = 0 if portfolioBias == PortfolioBias.Short else 1 self.optimizer = MinimumVariancePortfolioOptimizer(lower, upper, targetReturn) if optimizer is None else optimizer self.symbolDataBySymbol = {} # If the argument is an instance of Resolution or Timedelta # Redefine rebalancingFunc rebalancingFunc = rebalance if isinstance(rebalance, int): rebalance = Extensions.ToTimeSpan(rebalance) if isinstance(rebalance, timedelta): rebalancingFunc = lambda dt: dt + rebalance if rebalancingFunc: self.SetRebalancingFunc(rebalancingFunc)
class MeanVarianceOptimizationPortfolioConstructionModel( PortfolioConstructionModel): def __init__(self, rebalancingParam=Resolution.Daily, lookback=1, period=63, resolution=Resolution.Daily, targetReturn=0.02, optimizer=None): """Initialize the model Args: rebalancingParam: Rebalancing parameter. If it is a timedelta, date rules or Resolution, it will be converted into a function. If None will be ignored. The function returns the next expected rebalance time for a given algorithm UTC DateTime. The function returns null if unknown, in which case the function will be called again in the next loop. Returning current time will trigger rebalance. lookback(int): Historical return lookback period period(int): The time interval of history price to calculate the weight resolution: The resolution of the history price optimizer(class): Method used to compute the portfolio weights""" self.lookback = lookback self.period = period self.resolution = resolution self.optimizer = MinimumVariancePortfolioOptimizer( -1, 1, targetReturn) if optimizer is None else optimizer self.symbolDataBySymbol = {} # If the argument is an instance of Resolution or Timedelta # Redefine rebalancingFunc rebalancingFunc = rebalancingParam if isinstance(rebalancingParam, int): rebalancingParam = Extensions.ToTimeSpan(rebalancingParam) if isinstance(rebalancingParam, timedelta): rebalancingFunc = lambda dt: dt + rebalancingParam if rebalancingFunc: self.SetRebalancingFunc(rebalancingFunc) def ShouldCreateTargetForInsight(self, insight): if len( PortfolioConstructionModel.FilterInvalidInsightMagnitude( self.Algorithm, [insight])) == 0: return False symbolData = self.symbolDataBySymbol.get(insight.Symbol) if insight.Magnitude is None: self.algorithm.SetRunTimeError( ArgumentNullException( 'MeanVarianceOptimizationPortfolioConstructionModel does not accept \'None\' as Insight.Magnitude. Please checkout the selected Alpha Model specifications.' )) symbolData.Add(self.Algorithm.Time, insight.Magnitude) return True def DetermineTargetPercent(self, activeInsights): """ Will determine the target percent for each insight Args: Returns: """ targets = {} symbols = [insight.Symbol for insight in activeInsights] # Create a dictionary keyed by the symbols in the insights with an pandas.Series as value to create a data frame returns = { str(symbol): data.Return for symbol, data in self.symbolDataBySymbol.items() if symbol in symbols } returns = pd.DataFrame(returns) # The portfolio optimizer finds the optional weights for the given data weights = self.optimizer.Optimize(returns) weights = pd.Series(weights, index=returns.columns) # Create portfolio targets from the specified insights for insight in activeInsights: weight = weights[str(insight.Symbol)] targets[insight] = weight return targets def OnSecuritiesChanged(self, algorithm, changes): '''Event fired each time the we add/remove securities from the data feed Args: algorithm: The algorithm instance that experienced the change in securities changes: The security additions and removals from the algorithm''' # clean up data for removed securities super().OnSecuritiesChanged(algorithm, changes) for removed in changes.RemovedSecurities: symbolData = self.symbolDataBySymbol.pop(removed.Symbol, None) symbolData.Reset() # initialize data for added securities symbols = [x.Symbol for x in changes.AddedSecurities] history = algorithm.History(symbols, self.lookback * self.period, self.resolution) if history.empty: return tickers = history.index.levels[0] for ticker in tickers: symbol = SymbolCache.GetSymbol(ticker) if symbol not in self.symbolDataBySymbol: symbolData = self.MeanVarianceSymbolData( symbol, self.lookback, self.period) symbolData.WarmUpIndicators(history.loc[ticker]) self.symbolDataBySymbol[symbol] = symbolData class MeanVarianceSymbolData: '''Contains data specific to a symbol required by this model''' def __init__(self, symbol, lookback, period): self.symbol = symbol self.roc = RateOfChange(f'{symbol}.ROC({lookback})', lookback) self.roc.Updated += self.OnRateOfChangeUpdated self.window = RollingWindow[IndicatorDataPoint](period) def Reset(self): self.roc.Updated -= self.OnRateOfChangeUpdated self.roc.Reset() self.window.Reset() def WarmUpIndicators(self, history): for tuple in history.itertuples(): self.roc.Update(tuple.Index, tuple.close) def OnRateOfChangeUpdated(self, roc, value): if roc.IsReady: self.window.Add(value) def Add(self, time, value): item = IndicatorDataPoint(self.symbol, time, value) self.window.Add(item) @property def Return(self): return pd.Series(data=[(1 + float(x.Value))**252 - 1 for x in self.window], index=[x.EndTime for x in self.window]) @property def IsReady(self): return self.window.IsReady def __str__(self, **kwargs): return '{}: {:.2%}'.format(self.roc.Name, (1 + self.window[0])**252 - 1)
class MeanVarianceOptimizationPortfolioConstructionModel(PortfolioConstructionModel): def __init__(self, rebalancingParam = None, lookback = 1, period = 63, resolution = Resolution.Daily, optimizer = None): """Initialize the model Args: lookback(int): Historical return lookback period period(int): The time interval of history price to calculate the weight resolution: The resolution of the history price optimizer(class): Method used to compute the portfolio weights""" self.lookback = lookback self.period = period self.resolution = resolution self.optimizer = MinimumVariancePortfolioOptimizer() if optimizer is None else optimizer self.symbolDataBySymbol = {} self.pendingRemoval = [] # If the argument is an instance of Resolution or Timedelta # Redefine rebalancingFunc rebalancingFunc = rebalancingParam if isinstance(rebalancingParam, int): rebalancingParam = Extensions.ToTimeSpan(rebalancingParam) if isinstance(rebalancingParam, timedelta): rebalancingFunc = lambda dt: dt + rebalancingParam if rebalancingFunc: self.SetRebalancingFunc(rebalancingFunc) def CreateTargets(self, algorithm, insights): """ Create portfolio targets from the specified insights Args: algorithm: The algorithm instance insights: The insights to create portfolio targets from Returns: An enumerable of portfolio targets to be sent to the execution model """ # Always add new insights insights = PortfolioConstructionModel.FilterInvalidInsightMagnitude(algorithm, insights) for insight in insights: symbolData = self.symbolDataBySymbol.get(insight.Symbol) if insight.Magnitude is None: algorithm.SetRunTimeError(ArgumentNullException('MeanVarianceOptimizationPortfolioConstructionModel does not accept \'None\' as Insight.Magnitude. Please checkout the selected Alpha Model specifications.')) symbolData.Add(algorithm.Time, insight.Magnitude) targets = [] if not self.IsRebalanceDue(insights, algorithm.UtcTime): return targets for symbol in self.pendingRemoval: targets.append(PortfolioTarget.Percent(algorithm, symbol, 0)) self.pendingRemoval.clear() symbols = [insight.Symbol for insight in insights] if len(symbols) == 0: return targets # Create a dictionary keyed by the symbols in the insights with an pandas.Series as value to create a data frame returns = { str(symbol) : data.Return for symbol, data in self.symbolDataBySymbol.items() if symbol in symbols } returns = pd.DataFrame(returns) # The portfolio optimizer finds the optional weights for the given data weights = self.optimizer.Optimize(returns) weights = pd.Series(weights, index = returns.columns) # Create portfolio targets from the specified insights for insight in insights: weight = weights[str(insight.Symbol)] target = PortfolioTarget.Percent(algorithm, insight.Symbol, weight) if target is not None: targets.append(target) return targets def OnSecuritiesChanged(self, algorithm, changes): '''Event fired each time the we add/remove securities from the data feed Args: algorithm: The algorithm instance that experienced the change in securities changes: The security additions and removals from the algorithm''' # clean up data for removed securities super().OnSecuritiesChanged(algorithm, changes) for removed in changes.RemovedSecurities: self.pendingRemoval.append(removed.Symbol) symbolData = self.symbolDataBySymbol.pop(removed.Symbol, None) symbolData.Reset() # initialize data for added securities symbols = [ x.Symbol for x in changes.AddedSecurities ] history = algorithm.History(symbols, self.lookback * self.period, self.resolution) if history.empty: return tickers = history.index.levels[0] for ticker in tickers: symbol = SymbolCache.GetSymbol(ticker) if symbol not in self.symbolDataBySymbol: symbolData = self.MeanVarianceSymbolData(symbol, self.lookback, self.period) symbolData.WarmUpIndicators(history.loc[ticker]) self.symbolDataBySymbol[symbol] = symbolData class MeanVarianceSymbolData: '''Contains data specific to a symbol required by this model''' def __init__(self, symbol, lookback, period): self.symbol = symbol self.roc = RateOfChange(f'{symbol}.ROC({lookback})', lookback) self.roc.Updated += self.OnRateOfChangeUpdated self.window = RollingWindow[IndicatorDataPoint](period) def Reset(self): self.roc.Updated -= self.OnRateOfChangeUpdated self.roc.Reset() self.window.Reset() def WarmUpIndicators(self, history): for tuple in history.itertuples(): self.roc.Update(tuple.Index, tuple.close) def OnRateOfChangeUpdated(self, roc, value): if roc.IsReady: self.window.Add(value) def Add(self, time, value): item = IndicatorDataPoint(self.symbol, time, value) self.window.Add(item) @property def Return(self): return pd.Series( data = [(1 + float(x.Value))**252 - 1 for x in self.window], index = [x.EndTime for x in self.window]) @property def IsReady(self): return self.window.IsReady def __str__(self, **kwargs): return '{}: {:.2%}'.format(self.roc.Name, (1 + self.window[0])**252 - 1)