# 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.
#
# QC implementation changes:
#   - instead of GARCH model volatility, we have used simple historical volatility. 
            
from math import sqrt
from AlgorithmImports import *
import numpy as np
import pandas as pd

class TimeSeriesMomentum(QCAlgorithm):

    def Initialize(self) -> None:
        self.SetStartDate(2000, 1, 1)
        self.SetCash(10_000_000)

        self.symbols: List[str] = [
                        "CME_S1",   # Soybean Futures, Continuous Contract
                        "CME_W1",   # Wheat Futures, Continuous Contract
                        "CME_SM1",  # Soybean Meal Futures, Continuous Contract
                        "CME_BO1",  # Soybean Oil Futures, Continuous Contract
                        "CME_C1",   # Corn Futures, Continuous Contract
                        "CME_O1",   # Oats Futures, Continuous Contract
                        "CME_LC1",  # Live Cattle Futures, Continuous Contract
                        "CME_FC1",  # Feeder Cattle Futures, Continuous Contract
                        "CME_LN1",  # Lean Hog Futures, Continuous Contract
                        "CME_GC1",  # Gold Futures, Continuous Contract
                        "CME_SI1",  # Silver Futures, Continuous Contract
                        "CME_PL1",  # Platinum Futures, Continuous Contract
                        "CME_CL1",  # Crude Oil Futures, Continuous Contract
                        "CME_HG1",  # Copper Futures, Continuous Contract
                        "CME_LB1",  # Random Length Lumber Futures, Continuous Contract
                        "CME_NG1",  # Natural Gas (Henry Hub) Physical Futures, Continuous Contract
                        "CME_PA1",  # Palladium Futures, Continuous Contract 
                        "CME_RR1",  # Rough Rice Futures, Continuous Contract
                        "CME_DA1",  # Class III Milk Futures
                        "CME_RB1",  # Gasoline Futures, Continuous Contract
                        "CME_KW1",  # Wheat Kansas, Continuous Contract

                        "ICE_CC1",  # Cocoa Futures, Continuous Contract 
                        "ICE_CT1",  # Cotton No. 2 Futures, Continuous Contract
                        "ICE_KC1",  # Coffee C Futures, Continuous Contract
                        "ICE_O1",   # Heating Oil Futures, Continuous Contract
                        "ICE_OJ1",  # Orange Juice Futures, Continuous Contract
                        "ICE_SB1",  # Sugar No. 11 Futures, Continuous Contract
                        "ICE_RS1",  # Canola Futures, Continuous Contract
                        "ICE_GO1",  # Gas Oil Futures, Continuous Contract
                        "ICE_WT1",  # WTI Crude Futures, Continuous Contract
                                                
                        "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
                        "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_MD1",      # E-mini S&P MidCap 400 Futures
                        
                        "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
                        "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.period: int = 12 * 21
        self.SetWarmUp(self.period, Resolution.Daily)
        
        self.targeted_volatility: float = .1
        self.vol_target_period: int = 60
        self.leverage_cap: int = 4
        leverage: int = 20
        
        # Daily rolled data.
        self.data: Dict[str, RollingWindow] = {}
        
        for symbol in self.symbols:
            # Back adjusted and spliced data import.
            data: Security = self.AddData(QuantpediaFutures, symbol, Resolution.Daily)
            
            data.SetFeeModel(CustomFeeModel())
            data.SetLeverage(leverage)
            
            self.data[symbol] = RollingWindow[float](self.period)
        
        self.recent_month: int = -1
        self.Settings.MinimumOrderMarginPortfolioPercentage = 0.
        self.settings.daily_precise_end_time = False
    
    def OnData(self, slice: Slice) -> None:
        custom_data_last_update_date: Dict[str, datetime.date] = QuantpediaFutures.get_last_update_date()

        # Store daily data.
        for symbol in self.symbols:
            if slice.contains_key(symbol) and slice[symbol]:
                price = slice[symbol].Value
                self.data[symbol].Add(price)

        if self.recent_month == self.Time.month:
            return
        self.recent_month = self.Time.month
        
        # Performance and volatility data.
        performance_volatility: Dict[str, Tuple[float, float]] = {}
        daily_returns: Dict[str, float] = {}
        
        for symbol in self.symbols:
            if self.data[symbol].IsReady:
                # check if data is still coming
                if self.Securities[symbol].GetLastData() and self.time.date() > custom_data_last_update_date[symbol]:
                    self.liquidate(symbol)
                    continue

                back_adjusted_prices: np.ndarray = np.array([x for x in self.data[symbol]])
                performance: float = back_adjusted_prices[0] / back_adjusted_prices[-1] - 1
                daily_rets: np.ndarray = back_adjusted_prices[:-1] / back_adjusted_prices[1:] - 1
                
                back_adjusted_prices: np.ndarray = back_adjusted_prices[:self.vol_target_period]
                daily_rets: np.ndarray = back_adjusted_prices[:-1] / back_adjusted_prices[1:] - 1
                volatility_3M: float = np.std(daily_rets) * sqrt(252)
                daily_returns[symbol] = daily_rets[::-1][:self.vol_target_period]

                performance_volatility[symbol] = (performance, volatility_3M)

        if len(performance_volatility) == 0: return
    
        # Performance sorting.
        long: List[str] = [x[0] for x in performance_volatility.items() if x[1][0] > 0]
        short: List[str] = [x[0] for x in performance_volatility.items() if x[1][0] < 0]
        
        weight_by_symbol: Dict[str, float] = {}
        
        # Volatility weighting long and short leg separately.
        ls_leverage: List[float] = [] # long and short leverage
        
        for sym_i, symbols in enumerate([long, short]):
            total_volatility: float = sum([1/performance_volatility[x][1] for x in symbols])
            
            # Inverse volatility weighting.
            weights: np.ndarray = np.array([(1/performance_volatility[x][1]) / total_volatility for x in symbols])
            weights_sum: float = sum(weights)
            weights: float = weights/weights_sum
            
            df: DataFrame = pd.DataFrame()
            i: int = 0
            for symbol in symbols:
                df[str(symbol)] = [x for x in daily_returns[symbol]]
                weight_by_symbol[symbol] = weights[i] if sym_i == 0 else -weights[i]
                i += 1
            
            # volatility targeting
            portfolio_vol: float = np.sqrt(np.dot(weights.T, np.dot(df.cov() * 252, weights.T)))
            leverage: float = self.targeted_volatility / portfolio_vol
            leverage: float = min(self.leverage_cap, leverage) # cap max leverage
            ls_leverage.append(leverage)

        # Trade execution.
        invested: List[str] = [x.Key.Value for x in self.Portfolio if x.Value.Invested]
        for symbol in invested:
            if symbol not in long + short:
                self.Liquidate(symbol)

        for symbol, w in weight_by_symbol.items():
            if slice.contains_key(symbol) and slice[symbol]:
                if w >= 0:
                    self.SetHoldings(symbol, w*ls_leverage[0])
                    # self.SetHoldings(symbol, w)
                else:
                    self.SetHoldings(symbol, w*ls_leverage[1])
                    # self.SetHoldings(symbol, w)
            
# Quantpedia data.
# NOTE: IMPORTANT: Data order must be ascending (datewise)
class QuantpediaFutures(PythonData):
    _last_update_date: Dict[Symbol, datetime.date] = {}
    
    @staticmethod
    def get_last_update_date() -> Dict[Symbol, datetime.date]:
       return QuantpediaFutures._last_update_date

    def GetSource(self, config, date, isLiveMode):
        return SubscriptionDataSource("data.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['back_adjusted'] = float(split[1])
        data['spliced'] = float(split[2])
        data.Value = float(split[1])

        if config.Symbol.Value not in QuantpediaFutures._last_update_date:
            QuantpediaFutures._last_update_date[config.Symbol.Value] = datetime(1,1,1).date()
        if data.Time.date() > QuantpediaFutures._last_update_date[config.Symbol.Value]:
            QuantpediaFutures._last_update_date[config.Symbol.Value] = data.Time.date()

        return data

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