The strategy involves sorting stocks into portfolios based on size and return predictors, computing value-weighted returns, and ranking 51 equity factors. Long/short positions are taken in the best/worst performers monthly.
ASSET CLASS: stocks | REGION: United States | FREQUENCY:
Monthly | MARKET: equities | KEYWORD: Momentum
I. STRATEGY IN A NUTSHELL
The strategy uses NYSE, AMEX, and Nasdaq stocks to build 51 equity factor portfolios based on size and return predictors. Factors are rebalanced monthly or semi-annually. Each month, the investor goes long on top-performing factors and short on underperforming ones, equally weighting both sides.
II. ECONOMIC RATIONALE
Factor momentum shows that well-performing factors continue to outperform. It remains strong after accounting for stock and industry momentum, offering consistent returns across time and market conditions. Unlike stock momentum, it performs well during market recoveries.
III. SOURCE PAPER
Factor Momentum [Click to Open PDF]
Arnott, R.; Clements, M.; Kalesnik, V.; Linnainmaa, J. — Research Affiliates, LLC; Los Angeles Capital Management; Research Affiliates Global Advisors; Dartmouth College – Tuck School of Business; National Bureau of Economic Research (NBER); Kepos Capital.
<Abstract>
Past industry returns predict future industry returns, and this predictability is at its strongest at the one-month horizon. We show that the cross section of factor returns shares this property and that industry momentum stems from factor momentum. Factor momentum transmits into the cross section of industry returns through variation in industries’ factor loadings. We show that momentum in “systematic industries,” mimicking portfolios built from factors, subsumes industry momentum as does momentum in industry-neutral factors. Industry momentum is therefore a byproduct of factor momentum, not vice versa. Momentum concentrates in its entirety in the first few highest-eigenvalue factors.
IV. BACKTEST PERFORMANCE
| Annualised Return | 10.49% |
| Volatility | 15.28% |
| Beta | 0.085 |
| Sharpe Ratio | 0.69 |
| Sortino Ratio | -0.341 |
| Maximum Drawdown | N/A |
| Win Rate | 47% |
V. FULL PYTHON CODE
from AlgorithmImports import *
#endregion
class FactorMomentum(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2000, 1, 1)
self.SetCash(100000)
# daily price data
self.data:Dict[str, float] = {}
self.period:int = 12 * 21
self.SetWarmUp(self.period, Resolution.Daily)
self.leverage:int = 10
self.traded_count:int = 8
csv_string_file:str = self.Download('data.quantpedia.com/backtesting_data/equity/quantpedia_strategies/backtest_end_year.csv')
lines:str = csv_string_file.split('\r\n')
last_id:None|str = None
for line in lines[1:]:
split:str = line.split(';')
id:str = str(split[0])
backtest_to:int = int(split[1])
data:QuantpediaEquity = self.AddData(QuantpediaEquity, id, Resolution.Daily)
data.SetLeverage(self.leverage)
data.SetFeeModel(CustomFeeModel())
self.data[id] = self.ROC(id, self.period, Resolution.Daily)
if not last_id:
last_id = id
self.recent_month:int = -1
self.Settings.MinimumOrderMarginPortfolioPercentage = 0.
def OnData(self, data):
if self.IsWarmingUp:
return
if self.Time.month == self.recent_month:
return
self.recent_month = self.Time.month
if self.Time.month != 1: return
_last_update_date:Dict[str, datetime.date] = QuantpediaEquity.get_last_update_date()
# calculate performance of those strategies
performance:Dict[str, float] = { x : self.data[x].Current.Value for x in self.data \
if self.data[x].IsReady and \
x in data and data[x] and \
_last_update_date[x] > self.Time.date() }
long:List[str] = []
short:List[str] = []
# performance sorting
if len(performance) >= self.traded_count*2:
sorted_by_perf:List[str] = sorted(performance.items(), key = lambda x: x[1], reverse = True)
long = [x[0] for x in sorted_by_perf[:self.traded_count]]
short = [x[0] for x in sorted_by_perf[-self.traded_count:]]
# trade execution
invested:List[str] = [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)
long_count:int = len(long)
short_count:int = len(short)
for symbol in long:
self.SetHoldings(symbol, 1 / long_count)
for symbol in short:
self.SetHoldings(symbol, -1 / short_count)
# Quantpedia strategy equity curve data.
# NOTE: IMPORTANT: Data order must be ascending (datewise)
class QuantpediaEquity(PythonData):
def GetSource(self, config, date, isLiveMode):
return SubscriptionDataSource("data.quantpedia.com/backtesting_data/equity/quantpedia_strategies/{0}.csv".format(config.Symbol.Value), SubscriptionTransportMedium.RemoteFile, FileFormat.Csv)
_last_update_date:Dict[str, datetime.date] = {}
@staticmethod
def get_last_update_date() -> Dict[str, datetime.date]:
return QuantpediaEquity._last_update_date
def Reader(self, config, line, date, isLiveMode):
data = QuantpediaEquity()
data.Symbol = config.Symbol
if not line[0].isdigit(): return None
split = line.split(';')
data.Time = datetime.strptime(split[0], "%Y-%m-%d") + timedelta(days=1)
data['close'] = float(split[1])
data.Value = float(split[1])
# store last update date
if config.Symbol.Value not in QuantpediaEquity._last_update_date:
QuantpediaEquity._last_update_date[config.Symbol.Value] = datetime(1,1,1).date()
if data.Time.date() > QuantpediaEquity._last_update_date[config.Symbol.Value]:
QuantpediaEquity._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"))VI. Backtest Performance
