“该策略涉及30种商品期货，每天根据标准化价格对其进行排名。投资者做多排名最低（L）的商品，做空排名最高（H）的商品，并每天进行重新平衡。”
资产类别: 差价合约、期货 | 地区: 全球 | 周期: 每日 | 市场: 大宗商品 | 关键词: 排名效应
I. 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
学术论文指出，如果标准化商品价格的分布是平稳的，那么排名较低、价格较低的资产的价格必然比排名较高、价格较高的资产的价格增长得更快。换句话说，排名效应将存在。
III. 来源论文
The Rank Effect for Commodities [点击查看论文]
- 里卡多·T·费恩霍尔茨（Ricardo T. Fernholz）和克里斯托弗·科赫（Christoffer Koch）。克莱蒙特·麦肯纳学院 – 罗伯特·戴经济与金融学院。达拉斯联邦储备银行。
<摘要>
我们发现了两个世纪以来商品市场中巨大且显著的低排名减高排名效应。这个异常现象并不反常，也不清楚如何才能通过套利消除它。我们使用非参数计量经济学方法证明，这种排名效应是平稳相对资产价格分布的必然结果。我们使用每日商品期货价格证实了这一预测，并表明由排名较低、价格较低的商品组成的投资组合比由排名较高、价格较高的商品组成的投资组合产生高出23%的年回报率。这些超额回报的夏普比率几乎是美国股票市场的两倍，但与市场风险不相关。与关于资产定价因子和异常现象的大量文献相反，我们的结果是结构性的，并且依赖于相对资产价格长期属性的最小且现实的假设。
IV. 回测表现
| 年化回报 | 23.2% |
| 波动率 | N/A |
| β值 | 0.146 |
| 夏普比率 | N/A |
| 索提诺比率 | 0.194 |
| 最大回撤 | N/A |
| 胜率 | 45% |
V. 完整的 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"))