趋势追随与动量结合在商品期货中的应用

“该策略交易商品期货，做多表现最佳的合约，做空表现较差的合约，采用风险平价加权方法，并基于12个月的业绩排名和6个月移动平均趋势筛选进行调整。”

资产类别: 期货 | 地区: 美国 | 周期: 每月 | 市场: 大宗商品 | 关键词: 趋势跟随、动量

I. 策略概要

该策略使用道琼斯-UBS 大宗商品超额回报指数，涵盖 28 种大宗商品，并通过相应的期货进行交易。每月根据过去 12 个月的表现对大宗商品进行四分位排序。投资组合包括表现最好的（赢家）和表现最差的（输家）大宗商品，并采用风险平价方法进行加权，权重与其 60 天波动率成反比。此外，应用趋势跟随过滤器：大宗商品需高于其 6 个月简单移动平均线才能被视为赢家，或低于该均线才能被视为输家。投资者对符合筛选标准的赢家做多，对输家做空，从而构建一个平衡且基于表现的投资组合，同时实现系统性风险管理。

II. 策略合理性

学术研究对趋势跟随策略的历史成功提出了多种解释，包括投资者对新闻的反应不足以及羊群行为。动量效应通常被归因于投资者的非理性行为，因为他们未能完全将新信息纳入交易价格。此外，动量投资者可能利用其他市场参与者的行为偏差（如羊群效应、过度反应、反应不足和确认偏误），以把握可预测的价格趋势并从中获利。

III. 来源论文

Trend Following, Risk Parity and Momentum in Commodity Futures [点击查看论文]

• 托马斯、克莱尔、西顿、史密斯，伦敦城市大学商学院，伦敦城市大学贝叶斯商学院，伦敦城市大学商学院，约克大学经济与相关研究系；澳大利亚国立大学（ANU）应用宏观经济分析中心（CAMA）

<摘要>

我们证明，将动量策略与趋势跟随策略结合应用于单个商品期货，可以构建出提供有吸引力的风险调整收益的投资组合，其表现优于单纯的动量策略。当我们将这些收益暴露于广泛的系统性风险来源时，发现稳健的阿尔法仍然存在。实验表明，采用风险平价投资组合加权对结果的影响有限，尤其有利于多空策略；相比之下，在风险调整收益和降低下行风险方面，趋势跟随方法的边际影响远远超过动量策略和风险平价调整。总体而言，该策略为商品期货投资提供了一种有吸引力的方法，并强调了趋势跟随策略在商品期货投资中的重要性。

IV. 回测表现

年化回报	14.7%
波动率	19.33%
β值	-0.045
夏普比率	0.76
索提诺比率	0.089
最大回撤	-31.87%
胜率	55%

V. 完整的 Python 代码

import numpy as np
from AlgorithmImports import *
class TrendfollowingwithMomentum(QCAlgorithm):
    def Initialize(self):
        self.SetStartDate(1991, 1, 1)
        self.SetCash(100000)
        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
            "ICE_RS1",  # Canola Futures, Continuous Contract
            "ICE_GO1",  # Gas Oil Futures, Continuous Contract
            "CME_RB2",  # Gasoline Futures, Continuous Contract
            "CME_KW2",  # Wheat Kansas, Continuous Contract
            "ICE_WT1",  # WTI Crude Futures, 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
        ]
        self.winners = []
        self.losers = []
        self.data = {}
        self.period = 12*21
        self.SetWarmUp(self.period)
     
        for symbol in self.symbols:
            data = self.AddData(QuantpediaFutures, symbol, Resolution.Daily)
            data.SetLeverage(10)
            data.SetFeeModel(CustomFeeModel())
            
            ma = self.SMA(symbol, 6*21, Resolution.Daily)
            self.data[symbol] = SymbolData(symbol, 60, self.period, ma)
        
        self.rebalance_flag: bool = False
        self.Schedule.On(self.DateRules.MonthEnd(self.symbols[0]), self.TimeRules.At(0, 0), self.Rebalance)
    
    def OnData(self, data):
        for symbol, symbol_data in self.data.items():
            if self.securities[symbol].get_last_data() and self.time.date() > QuantpediaFutures.get_last_update_date()[symbol]:
                self.liquidate(symbol)
                symbol_data.History.reset()
                continue
            symbol_obj = self.Symbol(symbol)
            if symbol_obj in data.Keys:
                if data[symbol_obj]:
                    price = data[symbol_obj].Value
                    if price != 0:
                        self.data[symbol].Update(price)
        
        if not self.rebalance_flag:
            return
        self.rebalance_flag = False
        targets: List[PortfolioTarget] = []
        for i, portfolio in enumerate([self.winners, self.losers]):
            for symbol_data in portfolio:
                if symbol_data[1].Weight != 0:
                    if symbol_data[0] in data and data[symbol_data[0]]:
                        targets.append(PortfolioTarget(symbol_data[0], ((-1) ** i) * symbol_data[1].Weight))
        self.SetHoldings(targets, True)
        self.winners.clear()
        self.losers.clear()
    def Rebalance(self):
        # Return sorting
        return_values = []
        for data in self.data.items():
            if data[1].IsReady():
                return_values.append(data[1].Return())
        
        if len(return_values) == 0: return
    
        high_percentile = np.percentile(return_values, 75)
        low_percentile = np.percentile(return_values, 25)
        winners_by_ret = list(data for data in self.data.items() if data[1].IsReady() and data[1].Return() > high_percentile)
        losers_by_ret = list(data for data in self.data.items() if data[1].IsReady() and data[1].Return() < low_percentile)
        
        # Weighting
        total_vol = sum((1.0/data[1].Volatility()) for data in winners_by_ret if data[1].IsReady()) + sum((1.0/data[1].Volatility()) for data in losers_by_ret if data[1].IsReady())
        for data in winners_by_ret + losers_by_ret:
            if data[1].IsReady():
                vol = data[1].Volatility()
                data[1].Weight = (1.0 / vol) / total_vol
        # Trend sorting
        self.winners = list(data for data in winners_by_ret if data[1].Price > data[1].MA.Current.Value)
        self.losers = list(data for data in losers_by_ret if data[1].Price < data[1].MA.Current.Value)
        self.rebalance_flag = True
class SymbolData:
    def __init__(self, symbol, volatility_lookback, return_lookback, ma):
        self.Symbol = symbol
        self.History = RollingWindow[float](return_lookback)
        self.Price = 0.0
        self.MA = ma
        self.Weight = 0.0
        self.Volatility_lookback = volatility_lookback
    def IsReady(self) -> bool:
        return self.History.IsReady
            
    def Update(self, value: float):
        self.Price = value
        self.History.Add(float(value))
    
    def Return(self) -> float:
        prices = [x for x in self.History]
        return prices[0] / prices[-1] - 1
    
    def Volatility(self) -> float:
        prices = np.array([x for x in self.History])[-self.Volatility_lookback:]
        returns = prices[:-1] / prices[1:] - 1
        return np.std(returns) * np.sqrt(252)
    
# 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):
        fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
        return OrderFee(CashAmount(fee, "USD"))