The strategy trades 18 futures contracts, going long on six top-performers and short on six underperformers, rebalancing semi-annually based on six-month momentum across currencies, stocks, and fixed income.
ASSET CLASS: CFDs, futures | REGION: Global | FREQUENCY:
6 Months | MARKET: bonds, currencies, equities | KEYWORD: Cross-Sectional, Momentum, Futures
I. STRATEGY IN A NUTSHELL
This strategy invests in 18 futures across currencies, stock indices, and fixed income. Every six months, futures are ranked by past performance, with long positions in the top six and short positions in the bottom six. The equally weighted portfolio is rebalanced semi-annually to capture momentum across asset classes.
II. ECONOMIC RATIONALE
Momentum arises from investor behavioral biases such as herding, overreaction, and confirmation bias. It is also explained by macroeconomic inefficiencies, where variations in the price of risk across assets during the business cycle create exploitable trends.
III. SOURCE PAPER
The Financial Futures Momentum [Click to Open PDF]
Ayora, Torro
<Abstract>
The momentum strategy is the most famous anomaly arguing against thehypothesis offinancial market efficiency. In this chapter, the momentumstrategy produces a significant abnormal return for holding periods ofsixmonths and one year using financial futures (stock indexes, currencies, andfixed income). Furthermore, this study characterizes those futures con-tracts that contribute to the momentum strategy return. When the sampleis split in two groups, depending on the level ofvolatility, a significantly higher return is obtained in the high volatility group. Moreover, when thesample offutures is split in four groups, depending on the trading volumeand open interest levels, those contracts with high trading volume and low open interest report the best momentum performance.
IV. BACKTEST PERFORMANCE
| Annualised Return | 6.49% |
| Volatility | 12.91% |
| Beta | -0.024 |
| Sharpe Ratio | 0.5 |
| Sortino Ratio | -0.032 |
| Maximum Drawdown | N/A |
| Win Rate | 54% |
V. FULL PYTHON CODE
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 dataVI. Backtest Performance
