# https://quantpedia.com/strategies/combining-smart-factors-momentum-and-market-portfolio/
#
# The investment universe consists of factors from the Alpha Architect’s Factor Investing Data Library (factor for all major investment styles such as Value, Quality, Momentum, Size and Volatility)
# based on the top 1500 US stocks. Firstly construct the fast and slow signals for each factor. The fast signal is the past one-month return, and the slow signal is the past twelve-months return.
# For each type of signal, to obtain the weights, cross-sectionally rank signals’ based on their absolute values. The weight for the individual slow or fast signal is equal to the corresponding rank 
# divided by the sum of all ranks and multiplied by the signal’s sign (equations 3 and 4 in the paper). For the dynamically blended strategy (smart factors strategy), each factor has a final weight of
# three-quarters of the weight of fast signal plus one-quarter of the weight of slow signal (equation 12). Nextly, consider the top 1500 US stocks as the market portfolio. The combined smart factors 
# and market strategy finds the weights of the market and factor portfolio using past moving averages of the returns. The combined strategy looks back on the past twelve months, and twelve MAs of the 
# returns. Suppose the MA for active investing (factor momentum) is larger than MA for market portfolio, then the active investing scores one point. Otherwise, the market portfolio gets one point. 
# Therefore, each month, the weight of the factor momentum and market portfolio is determined by the number of “winning” (loosing) moving averages (equations 13 and 14). The strategy is rebalanced monthly.
#
# QC Implementation:
#   - IWM etf is used as 'market' portfolio.

import numpy as np

class CombiningSmartFactorsMomentumandMarketPortfolio(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2000, 1, 1)
        self.SetCash(100000)
        
        self.symbols = {
            'momentum' : 'US_EQUAL_DECILE_1500_12_2m_L_S',
            'value' : 'US_EQUAL_DECILE_1500_B_M_L_S',
            'quality' : 'US_EQUAL_DECILE_1500_ROA_L_S',
            'size' : 'US_EQUAL_DECILE_1500_Size_L_S',
            'volatility' : 'US_EQUAL_DECILE_1500_Volatility_L_S',
            }
        
        # monthly price data
        self.data = {}
        self.long_period = 13
        self.short_period = 2
        
        self.monthly_returns = {}
        self.monthly_returns_period = 12
        
        for symbol, equity_symbol in self.symbols.items():
            data = self.AddData(USEquity, equity_symbol, Resolution.Daily)
            data.SetLeverage(10)
            data.SetFeeModel(CustomFeeModel(self))
            self.data[symbol] = RollingWindow[float](self.long_period)
            
        self.market = self.AddEquity("IWM", Resolution.Daily).Symbol
        self.data[self.market] = RollingWindow[float](self.short_period)
        
        self.monthly_returns['smart_factors'] = RollingWindow[float](self.monthly_returns_period)
        self.monthly_returns['market'] = RollingWindow[float](self.monthly_returns_period)
        
        self.selection_flag = False
        self.Schedule.On(self.DateRules.MonthStart(self.market), self.TimeRules.AfterMarketOpen(self.market), self.Rebalance)
        
    def OnSecuritiesChanged(self, changes):
        for security in changes.AddedSecurities:
            security.SetFeeModel(CustomFeeModel(self))
            security.SetLeverage(5)
            
    def OnData(self, data):
        # store factor monthly prices
        for symbol, equity_symbol in self.symbols.items():
            if equity_symbol in data and data[equity_symbol]:
                price = data[equity_symbol].Value
                self.data[symbol].Add(price)
        
        # store market prices
        if self.market in data and data[self.market]:
            market_price = data[self.market].Value
            self.data[self.market].Add(market_price)
            
    def Rebalance(self):
        slow_momentum = {}
        fast_momentum = {}
        
        # calculate both momentum values
        for symbol, equity_symbol in self.symbols.items():
            if self.data[symbol].IsReady:
                slow_momentum[symbol] = self.data[symbol][0] / self.data[symbol][self.long_period-1] - 1
                fast_momentum[symbol] = self.data[symbol][0] / self.data[symbol][1] - 1
        
        if len(fast_momentum) != 0:
            # momentum ranking
            total_weight = {}
            
            # weights
            rank_sum = sum([x for x in range(1, len(slow_momentum)+1)])
            sorted_by_slow_momentum = sorted(slow_momentum.items(), key = lambda x: abs(x[1]), reverse = False)
            slow_weight = {}
            for i, (symbol, momentum) in enumerate(sorted_by_slow_momentum):
                rank = i+1
                slow_weight[symbol] = (rank / rank_sum) * np.sign(momentum)
    
            sorted_by_fast_momentum = sorted(fast_momentum.items(), key = lambda x: abs(x[1]), reverse = False)
            fast_weight = {}
            for i, (symbol, momentum) in enumerate(sorted_by_fast_momentum):
                rank = i+1
                fast_weight[symbol] = (rank / rank_sum) * np.sign(momentum)
            
            # total weight
            for symbol, equity_symbol in self.symbols.items():
                if symbol in slow_momentum and symbol in fast_momentum:
                    s_weight = slow_weight[symbol]
                    f_weight = fast_weight[symbol]
                    total_weight[symbol] = 0.75*f_weight + 0.25*s_weight
        
        # retrun calculation for market and smart factors
        if self.data[self.market].IsReady:
            market_return = self.data[self.market][0] / self.data[self.market][1] - 1
            self.monthly_returns['market'].Add(market_return)
            
            # smart factor return calculation
            smart_factors_return = 0
            for symbol, momentum_1M in fast_momentum.items():
                if symbol in total_weight:
                    w = total_weight[symbol]
                    symbol_ret = w*momentum_1M
                    smart_factors_return += symbol_ret
                    
            if smart_factors_return != 0:
                self.monthly_returns['smart_factors'].Add(smart_factors_return)
            
            score = {}
            traded_weight = {}
            
            # calculate 12 SMA's
            if self.monthly_returns['smart_factors'].IsReady and self.monthly_returns['market'].IsReady:
                score['smart_factors'] = 0
                score['market'] = 0
                for sma_period in range(1, 13):
                    factor_returns = [x for x in self.monthly_returns['smart_factors']][:sma_period]
                    market_returns = [x for x in self.monthly_returns['market']][:sma_period]
                    
                    factor_mean_return = np.mean(factor_returns)
                    market_mean_return = np.mean(market_returns)
                    
                    if factor_mean_return > market_mean_return:
                        score['smart_factors'] += 1
                    else:
                        score['market'] += 1
                
                total_score = score['market'] + score['smart_factors']
                if total_score != 0:
                    traded_weight['market'] = score['market'] / total_score
                    traded_weight['smart_factors'] = score['smart_factors'] / total_score
                    
                    # order execution
                    # market
                    self.SetHoldings(self.market, traded_weight['market'])
                    
                    # smart factors
                    for symbol, equity_symbol in self.symbols.items():
                        if symbol in total_weight:
                            w = total_weight[symbol]
                            self.SetHoldings(equity_symbol, traded_weight['smart_factors'] * w)
                            
class USEquity(PythonData):
    def GetSource(self, config, date, isLiveMode):
        return SubscriptionDataSource("data.quantpedia.com/backtesting_data/equity/us_ew_decile/{0}.csv".format(config.Symbol.Value), SubscriptionTransportMedium.RemoteFile, FileFormat.Csv)

    # File example.
    # date;equity
    # 1992-01-31;0.98
    def Reader(self, config, line, date, isLiveMode):
        data = USEquity()
        data.Symbol = config.Symbol
        
        if not line[0].isdigit(): return None
        split = line.split(';')
        
        # Prevent lookahead bias.
        data.Time = datetime.strptime(split[0], "%Y-%m-%d") + timedelta(days=1)
        data.Value = float(split[1])

        return data

# Custom fee model
class CustomFeeModel(FeeModel):
    def GetOrderFee(self, parameters):
        fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
        return OrderFee(CashAmount(fee, "USD"))