“该策略交易18种期货合约，包括7种货币、6种股票指数和5种固定收益工具，根据过去六个月的动量表现，选择6种表现最佳的期货做多、6种表现最差的期货做空，并每半年重新平衡。”
资产类别：差价合约（CFDs）、期货 | 区域：全球 | 频率：每6个月 | 市场：债券、外汇、股票 | 关键词：动量
I. 策略概述
- 交易范围：
- 排名与分配：
- 投资组合管理：
通过定期调整，该策略利用各资产类别中的动量趋势获利。
II. 策略合理性
- 行为金融学解释：
- 宏观经济理论解释：
结合这两种理论，横截面动量策略捕捉价格趋势，以从市场低效性中获利。
III. 论文来源
The Financial Futures Momentum [点击浏览原文]
- Ayora, Torro， 独立研究员
<摘要>
动量策略是对金融市场效率假设的最著名挑战之一。本文研究了股票指数、货币和固定收益的金融期货在六个月和一年持有期内的动量收益，并发现该策略在高波动性组中收益显著更高。此外，将期货样本按交易量和未平仓合约分为四组时，具有高交易量和低未平仓合约的期货表现出最佳动量收益。研究结果表明，动量策略不仅在多种资产类别中有效，还可通过交易量和波动性等特征进一步优化收益。
IV. 回测表现
| 年化收益率 | 6.49% |
| 波动率 | 12.91% |
| Beta | -0.024 |
| 夏普比率 | 0.5 |
| 索提诺比率 | -0.032 |
| 最大回撤 | N/A |
| 胜率 | 54% |
V. 完整python代码
from AlgorithmImports import *
class MomentumInFutures(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2000, 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_SF1", # Swiss Franc Futures, Continuous Contract #1
"CME_ES1", # E-mini S&P 500 Futures, Continuous Contract #1
"EUREX_FSMI1", # SMI Futures, Continuous Contract #1
"EUREX_FSTX1", # STOXX Europe 50 Index Futures, Continuous Contract #1
"LIFFE_FCE1", # CAC40 Index Futures, Continuous Contract #1
"LIFFE_Z1", # FTSE 100 Index Futures, Continuous Contract #1
"SGX_NK1", # SGX Nikkei 225 Index Futures, Continuous Contract #1
"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
"EUREX_FGBL1", # Euro-Bund (10Y) Futures, Continuous Contract #1
"SGX_JB1" # SGX 10-Year Mini Japanese Government Bond Futures
]
self.period = 6 * 21
self.count = 6
self.SetWarmup(self.period)
# Daily RoC data.
self.data = {}
for symbol in self.symbols:
data = self.AddData(QuantpediaFutures, symbol, Resolution.Daily)
data.SetFeeModel(CustomFeeModel())
data.SetLeverage(5)
self.data[symbol] = self.ROC(symbol, self.period, Resolution.Daily)
self.rebalance_flag: bool = False
self.month = 1
self.Schedule.On(self.DateRules.MonthStart(self.symbols[0]), self.TimeRules.At(0, 0), self.Rebalance)
def on_data(self, data: Slice) -> None:
if not self.rebalance_flag:
return
self.rebalance_flag = False
self.month += 1
if self.month > 6:
self.month = 1
if self.month != 6: return
# Return sorting.
long = []
short = []
sorted_by_return = sorted([x for x in self.data.items() if x[1].IsReady and self.Securities[x[0]].GetLastData() and self.Time.date() < QuantpediaFutures.get_last_update_date()[x[0]]], key = lambda x: x[1].Current.Value, reverse = True)
if len(sorted_by_return) >= self.count * 2:
long = [x[0] for x in sorted_by_return[:self.count]]
short = [x[0] for x in sorted_by_return[-self.count:]]
# 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:
if data.contains_key(symbol) and data[symbol]:
self.SetHoldings(symbol, 1 / len(long))
for symbol in short:
if data.contains_key(symbol) and data[symbol]:
self.SetHoldings(symbol, -1 / len(short))
def Rebalance(self):
self.rebalance_flag = True
# Custom fee model
class CustomFeeModel(FeeModel):
def GetOrderFee(self, parameters):
fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
return OrderFee(CashAmount(fee, "USD"))
# 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
