Overall Statistics
Total Trades
Average Win
Average Loss
Compounding Annual Return
Net Profit
Sharpe Ratio
Probabilistic Sharpe Ratio
Loss Rate
Win Rate
Profit-Loss Ratio
Annual Standard Deviation
Annual Variance
Information Ratio
Tracking Error
Treynor Ratio
Total Fees
import numpy as np

class GEMEnsembleAlphaModel(AlphaModel):
    """ If the S&P 500 had positive returns over the past X-months (positive trend) the strategy allocates to stocks 
    the next month; otherwise it allocates to bonds. 
    When the trend is positive for stocks the strategy holds the equity index with the strongest total return 
    over the same horizon. The Ensemble approach takes the average of all signals.

    def __init__(self, us_equity, foreign_equity, bond, resolution=Resolution.Daily):
        '''Initializes a new instance of the SmaAlphaModel class
            resolution: The reolution for our indicators
        self.us_equity = us_equity
        self.foreign_equity = foreign_equity
        self.bond = bond
        self.resolution = resolution
        self.symbolDataBySymbol = {}
        self.month = -1

    def Update(self, algorithm, data):
        '''This is called each time the algorithm receives data for (@resolution of) subscribed securities
        Returns: The new insights generated.
        THIS: analysis only occurs at month start, so any signals intra-month are disregarded.'''
        if self.month == algorithm.Time.month:
            return []
        self.month = algorithm.Time.month

        insights = []
        strategies = {}
        weights = dict.fromkeys([self.us_equity, self.foreign_equity, self.bond], 0)
        for symbol, symbolData in self.symbolDataBySymbol.items():
            strategies[symbol] = np.array([])
            for lookback in symbolData.momp.keys():
                strategies[symbol] = np.append(strategies[symbol], symbolData.momp[lookback].Current.Value)
        # 144 Strategies: select highest momentum equity if US > 0
        for value in strategies[self.us_equity]:
            if value >= 0:
                us_wins = sum(strategies[self.us_equity] >= strategies[self.foreign_equity])
                foreign_wins = sum(strategies[self.foreign_equity] > strategies[self.us_equity])
                weights[self.us_equity] += us_wins
                weights[self.foreign_equity] += foreign_wins
                bonds_win = len(strategies[self.us_equity])
                weights[self.bond] += bonds_win

        insights.append(Insight.Price(self.us_equity, Expiry.EndOfMonth, InsightDirection.Up, None, None, None, weights[self.us_equity]))
        insights.append(Insight.Price(self.foreign_equity, Expiry.EndOfMonth, InsightDirection.Up, None, None, None, weights[self.foreign_equity]))
        insights.append(Insight.Price(self.bond, Expiry.EndOfMonth, InsightDirection.Up, None, None, None, weights[self.bond]))   
        return insights

    def OnSecuritiesChanged(self, algorithm, changes):
        for added in changes.AddedSecurities:
            self.symbolDataBySymbol[added.Symbol] = SymbolData(added.Symbol, algorithm, self.resolution)

        for removed in changes.RemovedSecurities:
            symbolData = self.symbolDataBySymbol.pop(removed.Symbol, None)
            if symbolData:
                # Remove consolidator

class SymbolData:
    def __init__(self, symbol, algorithm, resolution):
        self.algorithm = algorithm
        self.Symbol = symbol
        self.momp = {}
        self.ma = {}
        for period in range(1, 13):
            self.momp[period] = MomentumPercent(period*21)
        for period in range(2,13):
            self.ma[period] = SimpleMovingAverage(period*21)

        # Warm up Indicators
        history = algorithm.History([self.Symbol], 21*13, resolution).loc[self.Symbol]
        # Use history to build our indicators
        for time, row in history.iterrows():
            for period, momp in self.momp.items():
                self.momp[period].Update(time, row["close"])
            for period, ma in self.ma.items():
                self.ma[period].Update(time, row["close"])

        # Setup indicator consolidator
        self.consolidator = TradeBarConsolidator(timedelta(1))
        self.consolidator.DataConsolidated += self.CustomDailyHandler
        algorithm.SubscriptionManager.AddConsolidator(self.Symbol, self.consolidator)
    def CustomDailyHandler(self, sender, consolidated):

        for period, momp in self.momp.items():
            self.momp[period].Update(consolidated.Time, consolidated.Close)
        for period, ma in self.ma.items():
            self.ma[period].Update(consolidated.Time, consolidated.Close)

    def dispose(self):
        self.algorithm.SubscriptionManager.RemoveConsolidator(self.Symbol, self.consolidator)
''' An ensemble approach to GEM - Global Equities Momentum.
from alpha_model import GEMEnsembleAlphaModel
from pcm import LeveragePCM

class GlobalTacticalAssetAllocation(QCAlgorithm):
    def Initialize(self):
        self.SetStartDate(2005, 1, 1)
        #self.SetEndDate(2020, 5, 20)
        self.Settings.FreePortfolioValuePercentage = 0.02
        self.SetBrokerageModel(BrokerageName.InteractiveBrokersBrokerage, AccountType.Margin)
        # PNQI, TLT
        tickers = ['SPY', 'EFA', 'TLT'] #for plotting
        us_equity = Symbol.Create('SPY', SecurityType.Equity, Market.USA)
        foreign_equity = Symbol.Create('EFA', SecurityType.Equity, Market.USA)
        bond = Symbol.Create('TLT', SecurityType.Equity, Market.USA)
        symbols = [us_equity, foreign_equity, bond]
        self.UniverseSettings.Resolution = Resolution.Daily
        self.AddUniverseSelection( ManualUniverseSelectionModel(symbols) )

        self.AddAlpha( GEMEnsembleAlphaModel(us_equity, foreign_equity, bond) )
        self.Settings.RebalancePortfolioOnSecurityChanges = False
        self.Settings.RebalancePortfolioOnInsightChanges = False
        self.SetPortfolioConstruction(LeveragePCM(self.RebalanceFunction, PortfolioBias.Long))
        self.lastRebalanceTime = None
        self.SetExecution( ImmediateExecutionModel() ) 
        self.AddRiskManagement( NullRiskManagementModel() )
        # Initialise plot
        assetWeightsPlot = Chart('AssetWeights %')
        for ticker in tickers:
            assetWeightsPlot.AddSeries(Series(ticker, SeriesType.Line, f'{ticker}%')) 

    def RebalanceFunction(self, time):

        return Expiry.EndOfMonth(self.Time)
    def OnData(self, data):
        # Update Plot
        for kvp in self.Portfolio:
                symbol = kvp.Key
                holding = kvp.Value 
                self.Plot('AssetWeights %', f"{str(holding.Symbol)}%", holding.HoldingsValue/self.Portfolio.TotalPortfolioValue)
## A simple m odification to add leverage factor to the InsightWeightingPortfolioConstructionModel
## This appears to be triggering everyday - when I thought it would trigger EOM?
class LeveragePCM(InsightWeightingPortfolioConstructionModel):
    leverage = 0.0
    def CreateTargets(self, algorithm, insights):
        targets = super().CreateTargets(algorithm, insights)
        return [PortfolioTarget(x.Symbol, x.Quantity*(1+self.leverage)) for x in targets]