time-series-momentum-effect.py

# 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):
        self.SetStartDate(2000, 1, 1)
        self.SetCash(10000000)

        self.symbols = [
            "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 = 12 * 21
        self.SetWarmUp(self.period, Resolution.Daily)

        self.targeted_volatility = 0.10
        self.vol_target_period = 60
        self.leverage_cap = 4

        # Daily rolled data.
        self.data = {}

        for symbol in self.symbols:
            data = None

            # Back adjusted and spliced data import.
            data = self.AddData(QuantpediaFutures, symbol, Resolution.Daily)

            data.SetFeeModel(CustomFeeModel())
            data.SetLeverage(20)

            self.data[symbol] = RollingWindow[float](self.period)

        self.recent_month = -1

    def OnData(self, data):
        # Store daily data.
        for symbol in self.symbols:
            if symbol in data and data[symbol]:
                price = data[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 = {}
        daily_returns = {}

        for symbol in self.symbols:
            if self.data[symbol].IsReady:
                if (
                    self.Securities[symbol].GetLastData()
                    and (
                        self.Time.date()
                        - self.Securities[symbol].GetLastData().Time.date()
                    ).days
                    < 5
                ):
                    back_adjusted_prices = np.array([x for x in self.data[symbol]])
                    performance = back_adjusted_prices[0] / back_adjusted_prices[-1] - 1
                    daily_rets = (
                        back_adjusted_prices[:-1] / back_adjusted_prices[1:] - 1
                    )

                    back_adjusted_prices = back_adjusted_prices[
                        : self.vol_target_period
                    ]
                    daily_rets = (
                        back_adjusted_prices[:-1] / back_adjusted_prices[1:] - 1
                    )
                    volatility_3M = 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 = [x[0] for x in performance_volatility.items() if x[1][0] > 0]
        short = [x[0] for x in performance_volatility.items() if x[1][0] < 0]

        weight_by_symbol = {}

        # Volatility weighting long and short leg separately.
        ls_leverage = []  # long and short leverage

        for sym_i, symbols in enumerate([long, short]):
            total_volatility = sum([1 / performance_volatility[x][1] for x in symbols])

            # Inverse volatility weighting.
            weights = np.array(
                [(1 / performance_volatility[x][1]) / total_volatility for x in symbols]
            )
            weights_sum = sum(weights)
            weights = weights / weights_sum

            df = pd.DataFrame()
            i = 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 = np.sqrt(
                np.dot(weights.T, np.dot(df.cov() * 252, weights.T))
            )
            leverage = self.targeted_volatility / portfolio_vol
            leverage = min(self.leverage_cap, leverage)  # cap max leverage
            ls_leverage.append(leverage)

        # Trade execution.
        invested = [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 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):
    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])

        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"))