Overall Statistics
import numpy as np

def Return(values):
    return (values[-1] - values[0]) / values[0]
def Volatility(values):
    values = np.array(values)
    returns = (values[1:]-values[:-1])/values[:-1]
    return np.std(returns)  

# Custom fee model
class CustomFeeModel(FeeModel):
    def GetOrderFee(self, parameters):
        fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
        return OrderFee(CashAmount(fee, "USD"))
# Quantpedia data
# NOTE: IMPORTANT: Data order must be ascending (datewise)
class QuantpediaFutures(PythonData):
    def GetSource(self, config, date, isLiveMode):
        return SubscriptionDataSource("https://quantpedia.com/backtesting_data/futures/{0}.csv".format(config.Symbol.Value), SubscriptionTransportMedium.RemoteFile, FileFormat.Csv)

    def Reader(self, config, line, date, isLiveMode):
        data = QuantpediaFutures()
        data.Symbol = config.Symbol
            if not line[0].isdigit(): return None
            split = line.split(';')
            data.Time = datetime.strptime(split[0], "%d.%m.%Y") + timedelta(days=1)
            data['settle'] = float(split[1])
            data.Value = float(split[1])
            return None
        return data
# https://quantpedia.com/strategies/consistent-momentum-strategy/
# The investment universe consists of stocks listed at NYSE, AMEX, and NASDAQ, whose price data (at least for the past 7 months) are available
# at the CRSP database. The investor creates a zero-investment portfolio at the end of the month t, longing stocks that are in the top decile 
# in terms of returns both in the period from t-7 to t-1 and from t-6 to t, while shorting stocks in the bottom decile in both periods (i.e. 
# longing consistent winners and shorting consistent losers). The stocks in the portfolio are weighted equally. The holding period is six months,
# with no rebalancing during the period. There is a one-month skip between the formation and holding period.

from collections import deque
import numpy as np
import fk_tools

class Consistent_Momentum_Strategy(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2000, 1, 1)
        self.SetEndDate(2019, 10, 1)

        self.course_count = 1000
        self.long = []
        self.short = []
        self.UniverseSettings.Resolution = Resolution.Daily
        self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
        self.symbol = 'SPY'
        self.AddEquity(self.symbol, Resolution.Daily)
        self.period = 7*21
        self.months = 0
        self.selection_flag = False
        self.Schedule.On(self.DateRules.MonthEnd(self.symbol), self.TimeRules.AfterMarketOpen(self.symbol), self.Rebalance)
    def OnSecuritiesChanged(self, changes):
        for security in changes.AddedSecurities:
    def CoarseSelectionFunction(self, coarse):
        if not self.selection_flag:
            return Universe.Unchanged
        selected = sorted([x for x in coarse if x.HasFundamentalData and x.Price > 5 and x.Market == 'usa'],
            key=lambda x: x.DollarVolume, reverse=True)
        return [x.Symbol for x in selected[:self.course_count]]

    def FineSelectionFunction(self, fine):
        momentum_t71 = {}
        momentum_t60 = {}
        for stock in fine:
            symbol = stock.Symbol
            hist = self.History([symbol], self.period, Resolution.Daily)
            if 'close' in hist.columns:
                closes = hist['close']
                if len(closes) == self.period:
                    # Return calc
                    closes_t71 = closes[:self.period - 21]
                    closes_t60 = closes[-self.period - 21:]
                    momentum_t71[symbol] = fk_tools.Return(closes_t71)
                    momentum_t60[symbol] = fk_tools.Return(closes_t60)
        if len(momentum_t71) == 0: return []
        if len(momentum_t60) == 0: return []

        # Momentum t-7 to t-1 sorting
        sorted_by_mom_t71 = sorted(momentum_t71.items(), key = lambda x: x[1], reverse = True)
        decile = int(len(sorted_by_mom_t71) / 10)
        high_by_mom_t71 = [x[0] for x in sorted_by_mom_t71[:decile]]
        low_by_mom_t71 = [x[0] for x in sorted_by_mom_t71[-decile:]]

        # Momentum t-6 to t sorting
        sorted_by_mom_t60 = sorted(momentum_t60.items(), key = lambda x: x[1], reverse = True)
        decile = int(len(sorted_by_mom_t60) / 10)
        high_by_mom_t60 = [x[0] for x in sorted_by_mom_t60[:decile]]
        low_by_mom_t60 = [x[0] for x in sorted_by_mom_t60[-decile:]]
        self.long = [x for x in high_by_mom_t71 if x in high_by_mom_t60]
        self.short = [x for x in low_by_mom_t71 if x in low_by_mom_t60]
        self.selection_flag = False
        return self.long + self.short
    def Rebalance(self):
        if self.months == 0:
            self.selection_flag = True
            self.months += 1
        if self.months == 1:
            # Trade execution and liquidation
            invested = [x.Key for x in self.Portfolio if x.Value.Invested]
            for symbol in invested:
                if symbol not in self.long + self.short:
            count = len(self.long + self.short)
            if count == 0: return
            for symbol in self.long:
                self.SetHoldings(symbol, 1/count)
            for symbol in self.short:
                self.SetHoldings(symbol, -1/count)
        self.months += 1
        if self.months == 6:
            self.months = 0