Quant Buffet放轻松,别过度思虑

利率期货的隔夜-日内每周反转策略

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学术论文

Market Closure and Short-Term Reversal

作者市场休市和短期反转 [点击查看论文]

机构
  • Centre for Economic Policy Research
  • ?Centre for Economic Policy Research (CEPR)
  • ?Imperial College Business School
  • University of Oxford
  • Quantitative BioSciences
  • ?CEPR (Centre for Economic Policy Research)
  • ?University of Oxford, Oxford-Man Institute of Quantitative Finance
  • Tsinghua University
  • ?Tsinghua University, School of Economics and Management

策略概要

该策略针对11种利率期货,包括联邦基金、欧洲美元和各种美国国债,其开盘价和收盘价来自TickData。投资者选择流动性最强的合约,重点关注最接近交割的期货。通过买入过去的周五至周一开盘输家,卖出赢家,形成零投资组合。使用论文中指定的公式确定单个债券的权重。投资组合在形成期后的周一开盘至周五收盘期间持有,并每周重新平衡。此方法旨在利用利率期货市场的短期低效率。

II. 策略合理性

当投资者对新闻反应过度,随后出现价格修正时,就会发生价格反转。市场休市,如隔夜和周末休息,以低流动性和交易活动为特征。Nagel(2012)表明,以VIX指数衡量的隔夜不确定性增加,在解释收盘至开盘(CO-OC)反转策略的利润方面起着关键作用,期货的结果比股票更强。此外,CO-OC反转模式与Hong和Wang(2000)的连续时间模型一致,其中市场休市期间的对冲需求促成了反转,解释了低流动性时期后的价格波动。

回测表现

波动率8.44%
夏普比率0.81
索提诺比率-6.567
胜率35%

完整 Python 代码

from AlgorithmImports import *
class OvernightIntradayWeeklyReversalInterestRate(QCAlgorithm):
def Initialize(self):
 self.SetStartDate(2000, 1, 1)
 self.SetCash(100000)
 
 self.symbols = [
     "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
     "CME_ED1",      # Eurodollar Futures, Continuous Contract #1
     "CME_FF1",      # 30-Day Fed Funds Futures, Continuous Contract #1
     "CME_US1"       # US Treasury Bond Futures, Continuous Contract #1
     "CME_UT1"       # US Ultra T-Bond Bond Futures, Continuous Contract #1                        
 ]
 
 self.friday_close = {}
 self.leverage = 1
 
 for symbol in self.symbols:
     data = self.AddData(QuantpediaFutures, symbol, Resolution.Daily)
     data.SetFeeModel(CustomFeeModel())
     data.SetLeverage(5)
     self.friday_close[symbol] = 0
 
 self.settings.minimum_order_margin_portfolio_percentage = 0.
def OnData(self, data):
 # Saturday -> Friday close available
 if self.Time.date().weekday() == 5:
     for symbol in self.symbols:
         if symbol in data and data[symbol]:
             price = data[symbol].Value
             if price != 0:
                 self.friday_close[symbol] = price
             
     self.Liquidate()
 # Tuesday -> Monday close available
 elif self.Time.date().weekday() == 1:
     returns = {}
     
     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
         if symbol in data and data[symbol]:
             price = data[symbol].Value
             if price != 0 and symbol in self.friday_close and self.friday_close[symbol] != 0:
                 returns[symbol] = price / self.friday_close[symbol] - 1
     
     self.friday_close.clear()
     if len(returns) == 0: 
         return
     
     ret_mean = np.mean([x[1] for x in returns.items()])
     
     weight = {}
     N = len(returns)
     for symbol in returns:
         weight[symbol] = -(1/N) * (returns[symbol] - ret_mean) * 100

     for symbol in weight:
         if data.contains_key(symbol) and data[symbol]:
             self.SetHoldings(symbol, self.leverage * weight[symbol])
# 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"))