“该策略交易11种利率期货，基于周五至周一的回报，买入输家，卖出赢家，每周重新平衡，头寸从周一开盘持有至周五收盘。”
资产类别: 差价合约、期货、互换 | 地区: 全球 | 周期: 每周 | 市场: 债券 | 关键词: 隔夜，反转，利率
I. 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。
III. 来源论文
市场休市和短期反转 [点击查看论文]
- Corte, Kosowski, Wang
<摘要>
一种买入过去隔夜回报较低的证券，卖出过去隔夜回报较高的证券的策略，在所有主要资产类别中都能产生可观的样本外日内回报和夏普比率。这种被称为隔夜-日内反转的策略，其平均回报大约是传统反转策略产生的回报的二到五倍。投资者异质性、情绪、市场不确定性和全市场流动性不足都无法解释这种隔夜-日内反转回报。我们的发现与资产类别特定的做市商流动性供给机制一致，我们发现横截面回报分散可以预测每个资产类别的策略回报。一个由市场和隔夜-日内反转因子组成的全球双因子模型，很好地解释了跨资产类别的多元化投资组合的日内回报变化。
IV. 回测表现
| 年化回报 | 6.88% |
| 波动率 | 8.44% |
| β值 | 0.002 |
| 夏普比率 | 0.81 |
| 索提诺比率 | -6.567 |
| 最大回撤 | N/A |
| 胜率 | 35% |
V. 完整的 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"))