期货市场中的跨行业动量策略

期货市场中的跨行业动量策略

该策略交易18种期货合约，包括7种货币、6种股票指数和5种固定收益工具，根据过去六个月的动量表现，选择6种表现最佳的期货做多、6种表现最差的期货做空，并每半年重新平衡。

I. 策略概述

• 交易范围：

• 货币期货（7种）：如欧元/美元、日元/美元等。

• 股票指数期货（6种）：如标普500、富时100等。

• 固定收益期货（5种）：如美国国债期货等。

• 排名与分配：

• 每六个月，根据过去六个月的表现对18种期货进行排名。

• 做多：表现最好的6种期货。

• 做空：表现最差的6种期货。

• 投资组合管理：

• 投资组合等权分配，确保资金在多头与空头之间均衡分布。

• 每半年根据最新表现排名重新平衡头寸。

通过定期调整，该策略利用各资产类别中的动量趋势获利。

II. 策略合理性

• 行为金融学解释：

• 投资者行为偏差（如羊群效应、过度反应和确认偏差）导致资产价格延续既有趋势，形成动量效应。

• 宏观经济理论解释：

• 动量收益可能来自于不同资产在商业周期中价格风险的套利低效性。

结合这两种理论，横截面动量策略捕捉价格趋势，以从市场低效性中获利。

III. 论文来源

The Financial Futures Momentum[点击浏览原文]

• Ayora, Torro， 独立研究员

<摘要>
动量策略是对金融市场效率假设的最著名挑战之一。本文研究了股票指数、货币和固定收益的金融期货在六个月和一年持有期内的动量收益，并发现该策略在高波动性组中收益显著更高。此外，将期货样本按交易量和未平仓合约分为四组时，具有高交易量和低未平仓合约的期货表现出最佳动量收益。研究结果表明，动量策略不仅在多种资产类别中有效，还可通过交易量和波动性等特征进一步优化收益。
</摘要>

IV. 回测表现

V. 完整Python代码

from AlgorithmImports import *

class MomentumInFutures(QCAlgorithm):
    def Initialize(self):
        self.SetStartDate(2000, 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_SF1",  # Swiss Franc 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
            "EUREX_FGBL1",  # Euro-Bund (10Y) Futures, Continuous Contract #1
            "SGX_JB1"  # SGX 10-Year Mini Japanese Government Bond Futures
        ]
        self.period = 6 * 21
        self.count = 6
        self.SetWarmup(self.period)

        # Daily RoC data.
        self.data = {}
        for symbol in self.symbols:
            data = self.AddData(QuantpediaFutures, symbol, Resolution.Daily)
            data.SetFeeModel(CustomFeeModel())
            data.SetLeverage(5)
            self.data[symbol] = self.ROC(symbol, self.period, Resolution.Daily)

        self.rebalance_flag: bool = False
        self.month = 1
        self.Schedule.On(self.DateRules.MonthStart(self.symbols[0]), self.TimeRules.At(0, 0), self.Rebalance)

    def on_data(self, data: Slice) -> None:
        if not self.rebalance_flag:
            return
        self.rebalance_flag = False
        self.month += 1
        if self.month > 6:
            self.month = 1
        if self.month != 6:
            return

        # Return sorting.
        long = []
        short = []
        sorted_by_return = sorted(
            [x for x in self.data.items() if x[1].IsReady and self.Securities[x[0]].GetLastData() and self.Time.date() < QuantpediaFutures.get_last_update_date()[x[0]]],
            key=lambda x: x[1].Current.Value,
            reverse=True
        )
        if len(sorted_by_return) >= self.count * 2:
            long = [x[0] for x in sorted_by_return[:self.count]]
            short = [x[0] for x in sorted_by_return[-self.count:]]

        # 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 in long:
            if data.contains_key(symbol) and data[symbol]:
                self.SetHoldings(symbol, 1 / len(long))
        for symbol in short:
            if data.contains_key(symbol) and data[symbol]:
                self.SetHoldings(symbol, -1 / len(short))

    def Rebalance(self):
        self.rebalance_flag = True

# 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):
    _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