# https://quantpedia.com/strategies/value-factor-effect-within-countries/
#
# The investment universe consists of 32 countries with easily accessible equity markets (via ETFs, for example). At the end of every year, 
# the investor calculates Shiller’s “CAPE” Cyclically Adjusted PE) ratio, for each country in his investment universe. CAPE is the ratio of 
# the real price of the equity market (adjusted for inflation) to the 10-year average of the country’s equity index (again adjusted for inflation). 
# The whole methodology is explained well on Shiller’s home page (http://www.econ.yale.edu/~shiller/data.htm) or
# http://turnkeyanalyst.com/2011/10/the-shiller-pe-ratio/). The investor then invests in the cheapest 33% of countries from his sample if those 
# countries have a CAPE below 15. The portfolio is equally weighted (the investor holds 0% cash instead of countries with a CAPE higher than 15)
# and rebalanced yearly.

#region imports
from AlgorithmImports import *
#endregion

class ValueFactorCAPEEffectwithinCountries(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2008, 1, 1)  
        self.SetCash(100000)

        self.symbols = {
            "Australia"     : "EWA",  # iShares MSCI Australia Index ETF
            "Brazil"        : "EWZ",  # iShares MSCI Brazil Index ETF
            "Canada"        : "EWC",  # iShares MSCI Canada Index ETF
            "Switzerland"   : "EWL",  # iShares MSCI Switzerland Index ETF
            "China"         : "FXI",  # iShares China Large-Cap ETF
            "France"        : "EWQ",  # iShares MSCI France Index ETF
            "Germany"       : "EWG",  # iShares MSCI Germany ETF 
            "Hong Kong"     : "EWH",  # iShares MSCI Hong Kong Index ETF
            "Italy"         : "EWI",  # iShares MSCI Italy Index ETF
            "Japan"         : "EWJ",  # iShares MSCI Japan Index ETF
            "Korea"         : "EWY",  # iShares MSCI South Korea ETF
            "Mexico"        : "EWW",  # iShares MSCI Mexico Inv. Mt. Idx
            "Netherlands"   : "EWN",  # iShares MSCI Netherlands Index ETF
            "South Africa"  : "EZA",  # iShares MSCI South Africe Index ETF
            "Singapore"     : "EWS",  # iShares MSCI Singapore Index ETF
            "Spain"         : "EWP",  # iShares MSCI Spain Index ETF
            "Sweden"        : "EWD",  # iShares MSCI Sweden Index ETF
            "Taiwan"        : "EWT",  # iShares MSCI Taiwan Index ETF
            "UK"            : "EWU",  # iShares MSCI United Kingdom Index ETF
            "USA"           : "SPY",  # SPDR S&P 500 ETF
            
            "Russia"        : "ERUS",  # iShares MSCI Russia ETF
            "Israel"        : "EIS",   # iShares MSCI Israel ETF
            "India"         : "INDA",  # iShares MSCI India ETF
            "Poland"        : "EPOL",  # iShares MSCI Poland ETF
            "Turkey"        : "TUR"    # iShares MSCI Turkey ETF
        }

        self.quantile:int = 3
        self.max_missing_days:int = 31
        self.leverage:int = 2

        for country, etf_symbol in self.symbols.items():
            data = self.AddEquity(etf_symbol, Resolution.Daily)
            data.SetLeverage(self.leverage)
            data.SetFeeModel(CustomFeeModel())
        
        # CAPE data import.
        self.cape_data = self.AddData(CAPE, 'CAPE',  Resolution.Daily).Symbol
            
        self.recent_month:int = -1
    
    def OnData(self, data:Slice) -> None:
        if self.Time.month == self.recent_month:
            return
        self.recent_month = self.Time.month

        if self.recent_month != 12:
            return
        
        price = {}
        for country, etf_symbol in self.symbols.items():
            if etf_symbol in data and data[etf_symbol]:
                # cape data is still comming in
                if self.Securities[self.cape_data].GetLastData() and (self.Time.date() - self.Securities[self.cape_data].GetLastData().Time.date()).days <= self.max_missing_days:
                    country_cape = self.Securities['CAPE'].GetLastData().GetProperty(country)
                    if country_cape < 15. and country_cape != 0.:
                        price[etf_symbol] = data[etf_symbol].Value

        long = []
        
        # Cape and price sorting.
        if len(price) >= self.quantile:
            sorted_by_price = sorted(price.items(), key = lambda x: x[1], reverse = True)
            tercile = int(len(sorted_by_price) / self.quantile)
            long = [x[0] for x in sorted_by_price[-tercile:]]
        
        # Trade execution.
        invested = [x.Key for x in self.Portfolio if x.Value.Invested]
        for symbol in invested:
            if symbol not in long:
                self.Liquidate(symbol)
        
        for symbol in long:
            if symbol in data and data[symbol]:
                self.SetHoldings(symbol, 1 / len(long))

# NOTE: IMPORTANT: Data order must be ascending (datewise)
# Data source: https://indices.barclays/IM/21/en/indices/static/historic-cape.app
class CAPE(PythonData):
    def GetSource(self, config, date, isLiveMode):
        return SubscriptionDataSource("data.quantpedia.com/backtesting_data/economic/cape_by_country.csv", SubscriptionTransportMedium.RemoteFile, FileFormat.Csv)

    _header_columns:List[str] = []

    def Reader(self, config, line, date, isLiveMode):
        data = CAPE()
        data.Symbol = config.Symbol
        
        if not line[0].isdigit():
            CAPE._header_columns = line.split(',')[1:]
            return None

        split = line.split(',')
        
        data.Time = datetime.strptime(split[0], "%Y-%m-%d") + timedelta(days=1)
        for i, col in enumerate(CAPE._header_columns):
            if split[i+1] != '':
                data[col] = float(split[i+1])
            else:
                data[col] = 0.
        
        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"))