Quant Buffet放轻松,别过度思虑

大宗商品的排名效应

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学术论文

作者The Rank Effect for Commodities [点击查看论文]

策略概要

该策略使用30种商品期货,从2010年的回测开始。每天,商品价格都被标准化为一个共同的起始值(2010年1月5日)。商品根据其标准化价格进行排名,排名较低(L)的商品位于最低五分位数,排名较高(H)的商品位于最高五分位数。投资者每天构建一个投资组合,通过在L商品中投资等额美元,并在H商品中卖空等额美元。投资组合每天重新平衡,利用排名在商品之间建立多空头寸。

II. 策略合理性

回测表现

索提诺比率0.194
胜率45%

完整 Python 代码

from AlgorithmImports import *
class RankEffectForCommodities(QCAlgorithm):
def Initialize(self):
 self.SetStartDate(2010, 1, 1)
 self.SetCash(100000)
 
 self.SetMaximumOrders(100000)
 self.quantile: int = 5
 
 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_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
 ]
 
 for symbol in self.symbols:
     data = self.AddData(QuantpediaFutures, symbol, Resolution.Daily)
     data.SetFeeModel(CustomFeeModel())
     data.SetLeverage(10)
 
 self.base_price = {}
 self.settings.daily_precise_end_time = False
def OnData(self, data):
 # base
 if self.Time.year == 2010 and self.Time.month == 1 and self.Time.day == 5:
     for symbol in self.symbols:
         if symbol in data and data[symbol]:
             self.base_price[symbol] = data[symbol].Value
 
 performance = {}
 
 for symbol in self.symbols:
     # Check if data is still coming.
     if self.securities[symbol].get_last_data() and self.time.date() > QuantpediaFutures.get_last_update_date()[symbol]:
         self.liquidate(symbol)
         continue
     symbol_obj = self.Symbol(symbol)
     if symbol_obj in data and data[symbol_obj]:
         price = data[symbol_obj].Value
         if price != 0 and symbol in self.base_price:
             base_price = self.base_price[symbol]
             ret = (price / base_price) - 1
             performance[symbol] = ret
 if len(performance) < self.quantile:
     return
 sorted_perf = sorted(performance.items(), key = lambda x: x[1], reverse = True)
 quintile = int(len(sorted_perf) / self.quantile)
 long = [x[0] for x in sorted_perf[-quintile:]]
 short = [x[0] for x in sorted_perf[:quintile]]
 
 targets: List[PortfolioTarget] = []
 for i, portfolio in enumerate([long, short]):
     for symbol in portfolio:
         if symbol in data and data[symbol]:
             targets.append(PortfolioTarget(symbol, ((-1) ** i) / len(portfolio)))
 
 self.SetHoldings(targets, True)
 # 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:
 #     self.SetHoldings(symbol, 1 / len(long))
 # for symbol in short:
 #     self.SetHoldings(symbol, -1 / len(short))
# 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"))