“W-L多空投资组合交易63种兑美元货币，基于滞后4周的超额回报，做多前20%的赢家，做空后20%的输家。”
资产类别: 差价合约、远期、期货、互换 | 地区: 全球 | 周期: 每周 | 市场: 外汇 | 关键词: 动量
I. 策略概要
W-L多空投资组合交易63种兑美元货币，使用4周的持有期和回顾期。滞后超额回报最高的20%的货币被归类为“赢家”，而最低的20%被归类为“输家”。该投资组合对兑美元升值幅度最大的货币建立多头头寸，对在4周内贬值幅度最大的货币建立空头头寸。该策略旨在通过利用过去的表现来预测未来的走势，从而利用短期货币趋势获利。
II. 策略合理性
我们发现，随着回顾期的增加，动量回报显著增加。短期动量回报在外汇市场的下跌状态（主要货币兑美元货币篮子贬值后的时期）期间较高。4周的回顾期产生最高的回报。动量回报在波动性方面是稳定的，并且在风险调整后的基础上更具吸引力。
III. 来源论文
每周货币回报中存在动量还是反转？[点击查看论文]
- 拉扎，马歇尔，维萨塔纳乔提，奥塔哥大学会计与金融系，梅西大学经济与金融学院，梅西大学经济与金融系
<摘要>
我们调查了在短期（一到四周）外汇汇率回报中，动量还是反转是主导现象。基于对63种新兴市场和发达市场货币的广泛样本，我们发现了动量而非反转的证据。动量回报高达每年9%。短期动量效应似乎是稳健的。回报在早期子时期更大，但在较近时期仍然存在。该策略在美国经济衰退和扩张时期，以及货币市场的上涨和下跌时期都是有利可图的。
IV. 回测表现
| 年化回报 | 7.1% |
| 波动率 | 8.1% |
| β值 | -0.049 |
| 夏普比率 | 0.88 |
| 索提诺比率 | -0.399 |
| 最大回撤 | N/A |
| 胜率 | 44% |
V. 完整的 Python 代码
from AlgorithmImports import *
class ShortTermMomentumCurrencies(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2010, 1, 1)
self.SetCash(100000)
self.symbols = [
"CME_AD1", # Australian Dollar Futures, Continuous Contract #1
"CME_BP1", # British Pound Futures, Continuous Contract #1
"CME_CD1", # Canadian Dollar Futures, Continuous Contract #1
"CME_EC1", # Euro FX Futures, Continuous Contract #1
"CME_JY1", # Japanese Yen Futures, Continuous Contract #1
"CME_MP1", # Mexican Peso Futures, Continuous Contract #1
"CME_NE1",# New Zealand Dollar Futures, Continuous Contract #1
"CME_SF1", # Swiss Franc Futures, Continuous Contract #1
]
self.period = 21
self.quantile = 5
self.SetWarmUp(self.period)
self.data = {}
for symbol in self.symbols:
data = self.AddData(QuantpediaFutures, symbol, Resolution.Daily)
data.SetLeverage(5)
data.SetFeeModel(CustomFeeModel())
self.data[symbol] = RollingWindow[float](self.period)
self.Schedule.On(self.DateRules.MonthStart(self.symbols[0]), self.TimeRules.At(0, 0), self.Rebalance)
def OnData(self, data):
for symbol in self.data:
symbol_obj = self.Symbol(symbol)
if symbol_obj in data.Keys:
if data[symbol_obj]:
price = data[symbol_obj].Value
if price != 0:
self.data[symbol].Add(price)
def Rebalance(self):
if self.IsWarmingUp: return
returns = {}
for symbol in self.data:
if self.data[symbol].IsReady:
# 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
ret = self.data[symbol][0] / self.data[symbol][self.period-1] - 1
returns[symbol] = ret
long = []
short = []
if len(returns) >= self.quantile:
# Return sorting.
sorted_by_return = sorted(returns.items(), key = lambda x: x[1], reverse = True)
quintile = int(len(sorted_by_return) / self.quantile)
long = [x[0] for x in sorted_by_return[:quintile]]
short = [x[0] for x in sorted_by_return[-quintile:]]
# Trade execution
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"))