商品因子投资组合的时机选择

“该策略在高于5日移动平均线时，投资于表现最佳的商品期货（6个月回报），使用市场择时过滤器来优化回报和管理风险。”

资产类别: 期货 | 地区: 全球 | 周期: 每日 | 市场: 大宗商品 | 关键词: 择时

I. 策略概要

该策略专注于35种商品期货合约，每月根据前一个月的回报将其分为三个等级。投资组合由过去6个月表现最佳的期货组成。5日移动平均线用作市场择时过滤器——仅在投资组合的表现超过其5日移动平均线时进行投资；否则，投资者退出市场。移动平均线过滤器可以有效地对按波动率、成交量、未平仓合约或过去表现排序的各种商品投资组合进行择时，其中6个月的表现标准是该策略的一个示例。

II. 策略合理性

学术论文发现，移动平均择时策略在经济衰退和投资者情绪高涨时期表现明显优于其他策略。这种优异表现并非归因于非理性的投资者行为，而是由于高涨的投资者情绪充当了高实际利率的代理，从而增强了该策略的有效性。使用亨里克森和默顿（1981）的市场择时模型，该论文得出结论，该策略的成功主要归功于其有效择时市场投资组合的能力，从而在不同的市场条件下捕捉机会并降低风险。

III. 来源论文

商品期货价格是否存在可利用的趋势？[点击查看论文]

• 韩，胡，北卡罗来纳大学夏洛特分校 – 金融学，武汉大学 – 经济与管理学院

<摘要>

我们提供的证据表明，当应用于商品期货投资组合时，简单的移动平均择时策略可以产生优于买入并持有策略的业绩。这种优异表现非常稳健。它可以承受期货市场的交易成本，它不集中在特定的子周期内，并且对卖空约束、移动平均滞后长度的替代设定以及期货价格连续时间序列的替代构建都具有稳健性。择时策略的优异表现在经济衰退期间和投资者情绪高涨时更为明显，这可能代表了高实际利率。最后，我们确认商品期货中移动平均择时策略的优异表现来自对市场投资组合的成功择时。

IV. 回测表现

年化回报	8.04%
波动率	11.3%
β值	0.14
夏普比率	0.71
索提诺比率	0.221
最大回撤	N/A
胜率	53%

V. 完整的 Python 代码

from collections import deque
from AlgorithmImports import *
import numpy as np
class TimingCommodityFactor(QCAlgorithm):
    def Initialize(self):
        self.SetStartDate(2002, 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.data = {}
        self.top = {}
        self.low = {}
        
        ret_period = 120
        vol_period = 60
        ma_period = 5
        
        self.SetWarmUp(max(ret_period, vol_period, ma_period))
        
        for symbol in self.symbols:
            data = self.AddData(QuantpediaFutures, symbol, Resolution.Daily)
            data.SetLeverage(5)
            data.SetFeeModel(CustomFeeModel())
            
            ma = self.SMA(symbol, ma_period, Resolution.Daily)
            self.data[symbol] = SymbolData(symbol, ret_period, vol_period, ma)
        self.Schedule.On(self.DateRules.MonthStart(self.symbols[0]), self.TimeRules.At(0, 0), self.Rebalance)
    def OnData(self, data):
        last_update_date = {}
        for symbol in self.data:
            # data is still coming
            if self.securities[symbol].get_last_data() and self.time.date() > QuantpediaFutures.get_last_update_date()[symbol]:
                self.liquidate(symbol)
                self.data[symbol].History.clear()
                continue
            symbol_obj = self.Symbol(symbol)
            if symbol_obj in data.Keys:
                if data[symbol_obj]:
                    price = data[symbol_obj].Value
                    self.data[symbol].Update(price)
                    last_update_date[symbol] = self.Time.date()
        
        if self.IsWarmingUp: return
    
        sma_top = list(data for data in self.top if data[1].IsReady() and data[1].Price > data[1].MA.Current.Value and data[0] in last_update_date)
        sma_low = list(data for data in self.low if data[1].IsReady() and data[1].Price < data[1].MA.Current.Value and data[0] in last_update_date)
        for data in sma_top:
            symbol = data[0]
            symbol_data = data[1]
            
            if not self.Portfolio[symbol].IsLong:
                if symbol_data.Price > symbol_data.MA.Current.Value:
                    if symbol_data.Weight != 0:
                        self.SetHoldings(symbol, symbol_data.Weight)
            elif self.Portfolio[symbol].IsLong:
                if symbol_data.Price <= symbol_data.MA.Current.Value:
                    self.Liquidate(symbol)
    
        for data in sma_low:
            symbol = data[0]
            symbol_data = data[1]
            if not self.Portfolio[symbol].IsShort:
                if symbol_data.Price < symbol_data.MA.Current.Value:
                    if symbol_data.Weight != 0:
                        self.SetHoldings(symbol, symbol_data.Weight)
            elif self.Portfolio[symbol].IsShort:
                if symbol_data.Price >= symbol_data.MA.Current.Value:
                    self.Liquidate(symbol)       
    def Rebalance(self):
        if self.IsWarmingUp: return
        self.Liquidate()
        sorted_by_ret = sorted([d for d in self.data.items() if d[1].IsReady()], key=lambda x: x[1].Return(), reverse = True)
        self.top = sorted_by_ret[:int(1/3 * len(sorted_by_ret))]
        self.low = sorted_by_ret[-int(1/3 * len(sorted_by_ret)):]
        
        # Weighting
        total_vol = sum((1.0/data[1].Volatility()) for data in self.top if data[1].IsReady()) + sum((1.0/data[1].Volatility()) for data in self.low if data[1].IsReady())
        for data in self.top + self.low:
            if data[1].IsReady():
                vol = data[1].Volatility()
                data[1].Weight = (1.0 / vol) / total_vol
class SymbolData:
    def __init__(self, symbol, ret_lookback, vol_lookback, ma):
        self.Symbol = symbol
        self.History = deque(maxlen=max(ret_lookback, vol_lookback))
        self.Price = 0.0
        self.MA = ma
        self.Weight = 0
        
        self.ret_lookback = ret_lookback
        self.vol_lookback = vol_lookback
    def IsReady(self):
        return len(self.History) == self.History.maxlen
        
    def Update(self, value):
        self.Price = float(value)
        self.History.append(float(value))
    def Return(self):
        prices = np.array(self.History)[-self.ret_lookback:]
        return (prices[-1]-prices[0])/prices[0]
    def Volatility(self):
        prices = np.array(self.History)[-self.vol_lookback:]
        returns = (prices[1:]-prices[:-1])/prices[:-1]
        return np.std(returns)
# 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"))