# https://quantpedia.com/strategies/time-series-momentum-effect/
#
# The investment universe consists of 24 commodity futures, 12 cross-currency pairs (with 9 underlying currencies), 9 developed equity indices, and 13 developed
# government bond futures.
# Every month, the investor considers whether the excess return of each asset over the past 12 months is positive or negative and goes long on the contract if it is 
# positive and short if negative. The position size is set to be inversely proportional to the instrument’s volatility. A univariate GARCH model is used to estimated 
# ex-ante volatility in the source paper. However, other simple models could probably be easily used with good results (for example, the easiest one would be using 
# historical volatility instead of estimated volatility). The portfolio is rebalanced monthly.

from datetime import datetime
import numpy as np
from collections import deque
import math

class Time_Series_Momentum(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2000, 1, 1)
        self.SetEndDate(2019, 1, 1)
        self.SetCash(100000)

        self.symbols = ["CME_AD1", # Australian Dollar Futures, Continuous Contract #1
                        "CME_BP1", # British Pound Futures, Continuous Contract #1
                        "CME_CD1", # Canadian Dollar Futures, Continuous Contract #1
                        "CME_EC1", # Euro FX Futures, Continuous Contract #1
                        "CME_JY1", # Japanese Yen Futures, Continuous Contract #1
                        "CME_MP1", # Mexican Peso Futures, Continuous Contract #1
                        #"CME_NE1",# New Zealand Dollar Futures, Continuous Contract #1    # Short history
                        "CME_SF1", # Swiss Franc Futures, Continuous Contract #1
                    
                        "ICE_DX1",      # US Dollar Index Futures, Continuous Contract #1
                        "CME_NQ1",      # E-mini NASDAQ 100 Futures, Continuous Contract #1
                        "EUREX_FDAX1",  # DAX Futures, Continuous Contract #1
                        "CME_ES1",      # E-mini S&P 500 Futures, Continuous Contract #1
                        "EUREX_FSMI1",  # SMI Futures, Continuous Contract #1
                        "EUREX_FSTX1",  # STOXX Europe 50 Index Futures, Continuous Contract #1
                        "LIFFE_FCE1",   # CAC40 Index Futures, Continuous Contract #1
                        "LIFFE_Z1",     # FTSE 100 Index Futures, Continuous Contract #1
                        "SGX_NK1",      # SGX Nikkei 225 Index Futures, Continuous Contract #1
                    
                        "CME_TY1",      # 10 Yr Note Futures, Continuous Contract #1
                        "CME_FV1",      # 5 Yr Note Futures, Continuous Contract #1
                        "CME_TU1",      # 2 Yr Note Futures, Continuous Contract #1
                        #"ASX_XT1",     # 10 Year Commonwealth Treasury Bond Futures, Continuous Contract #1   # 'Settlement price' instead of 'settle' on quandl. 
                        #"ASX_YT1",     # 3 Year Commonwealth Treasury Bond Futures, Continuous Contract #1    # 'Settlement price' instead of 'settle' on quandl.
                        "EUREX_FGBL1",  # Euro-Bund (10Y) Futures, Continuous Contract #1
                        #"EUREX_FBTP1", # Long-Term Euro-BTP Futures, Continuous Contract #1   # Short history
                        "EUREX_FGBM1",  # Euro-Bobl Futures, Continuous Contract #1
                        "EUREX_FGBS1",  # Euro-Schatz Futures, Continuous Contract #1 
                        "SGX_JB1",      # SGX 10-Year Mini Japanese Government Bond Futures
                        "LIFFE_R1"      # Long Gilt Futures, Continuous Contract #1
                        #"MX_CGB1",     # Ten-Year Government of Canada Bond Futures, Continuous Contract #1    # 'Settlement price' instead of 'settle' on quandl.
                        ]

        self.lookup_period = 12*21
        self.data = {}
        self.SetWarmUp(self.lookup_period)
        
        # True -> Quantpedia data
        # False -> Quandl free data
        self.use_quantpedia_data = True
        
        if not self.use_quantpedia_data:
            self.symbols = ['CHRIS/' + x for x in self.symbols]
        
        for symbol in self.symbols:
            data = None
            if self.use_quantpedia_data:
                data = self.AddData(QuantpediaFutures, symbol, Resolution.Daily)
            else:
                data = self.AddData(QuandlFutures, symbol, Resolution.Daily)
            #data.SetLeverage(2)
            self.data[symbol] = deque(maxlen=self.lookup_period)
        
        self.Schedule.On(self.DateRules.MonthStart(self.symbols[0]), self.TimeRules.AfterMarketOpen(self.symbols[0]), self.Rebalance)
    
    def OnData(self, data):
        for symbol in self.symbols:
            if self.Securities.ContainsKey(symbol):
                price = self.Securities[symbol].Price
                if price != 0:
                    self.data[symbol].append(price)
        
    def Rebalance(self):
        if self.IsWarmingUp: return

        # Return sorting
        returns = {}
        volatility = {}
        for symbol in self.symbols:
            if len(self.data[symbol]) == self.data[symbol].maxlen:
                prices = [x for x in self.data[symbol]]
                returns[symbol] = self.Return(prices)
                
                prices = prices[-60:]
                volatility[symbol] = self.Volatility(prices)
            #else: return

        if len(returns) == 0: return
    
        long = [x[0] for x in returns.items() if x[1] > 0]
        short = [x[0] for x in returns.items() if x[1] < 0]
        
        # Volatility weighting
        total_vol = sum([1/volatility[x] for x in long + short])
        if total_vol == 0: return
        
        weight = {}
        for symbol in long + short:
            vol = volatility[symbol]
            if vol != 0:
                weight[symbol] = (1.0 / vol) / total_vol
            else: 
                weight[symbol] = 0

        self.Liquidate()
        for symbol in long:
            self.SetHoldings(symbol, weight[symbol])
        for symbol in short:
            self.SetHoldings(symbol, -weight[symbol])
    
    def Return(self, history):
        return (history[-1] - history[0]) / history[0]
    
    def Volatility(self, history):
        prices = np.array(history)
        returns = (prices[1:]-prices[:-1])/prices[:-1]
        return np.std(returns)  

# Quantpedia data
class QuantpediaFutures(PythonData):
    def GetSource(self, config, date, isLiveMode):
        return SubscriptionDataSource("https://quantpedia.com/backtesting_data/futures/{0}.csv".format(config.Symbol), SubscriptionTransportMedium.RemoteFile, FileFormat.Csv)

    def Reader(self, config, line, date, isLiveMode):
        data = QuantpediaFutures()
        data.Symbol = config.Symbol
        
        try:
            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])
        except:
            return None
            
        return data

# Quandl free data
class QuandlFutures(PythonQuandl):
    def __init__(self):
        self.ValueColumnName = "settle"