The strategy trades 90 futures/ETFs, allocating 20% to asset classes, shifting to T-Bills in downtrends, investing in top-performing sub-components during uptrends, with equal weighting and monthly rebalancing.
ASSET CLASS: ETFs | REGION: Global | FREQUENCY:
Monthly | MARKET: bonds, commodities, equities, REITs | KEYWORD: Momentum, Trend, Following, Global, Asset, Allocation
I. STRATEGY IN A NUTSHELL
The strategy invests across ~90 futures and ETFs spanning developed equities, emerging equities, bonds, commodities, and REITs. Each asset class receives a 20% allocation, which is adjusted monthly based on a ten-month trend filter. If an asset class is in a downtrend, its allocation shifts to U.S. T-Bills; if in an uptrend, its sub-components are ranked by 12-month risk-adjusted returns, and the top half are selected. Both asset classes and sub-components are equally weighted, with monthly rebalancing ensuring disciplined and diversified exposure.
II. ECONOMIC RATIONALE
The economic rationale rests on the benefits of trend following and momentum. A systematic trend filter helps counter behavioral biases by cutting losers early while allowing winners to compound. This reduces exposure to large downside risks and mitigates negative fat tails. At the same time, exploiting the momentum effect enhances long-term returns, creating a more robust risk-return profile across diverse asset classes.
III. SOURCE PAPER
The Trend is Our Friend: Risk Parity, Momentum and Trend Following in Global Asset Allocation [Click to Open PDF]
Thomas, Clare, Smith, Seaton, City, City University London – The Business School, University of London – Bayes Business School, University of York – Department of Economics and Related Studies; Australian National University (ANU) – Centre for Applied Macroeconomic Analysis (CAMA), City University London – The Business School
<Abstract>
We examine the effectiveness of applying a trend following methodology to global asset allocation between equities, bonds, commodities and real estate. The application of trend following offers a substantial improvement in risk-adjusted performance compared to traditional buy-and-hold portfolios. We also find it to be a superior method of asset allocation than risk parity. We believe the discipline of trend following overcomes many of the behavioural biases investors succumb to, such as regret and herding. The other side of behavioural biases is that they may be exploited by investors: the clearest example of this is momentum investing where herding leads to continuation of returns and has been identified across many asset classes. Also, momentum and trend following have often been used interchangeably although the former is a relative concept and the latter absolute. By combining the two we find that one can achieve the higher return levels associated with momentum portfolios but with much reduced volatility and drawdowns due to trend following. We compare the performance of selected strategies using measures based on the utility function of a representative investor. These results reinforce the superiority of combining trend following with momentum strategies. We observe that a flexible asset allocation strategy that allocates capital to the best performing instruments irrespective of asset class enhances this further.
IV. BACKTEST PERFORMANCE
| Annualised Return | 10.78% |
| Volatility | 8.25% |
| Beta | 0.224 |
| Sharpe Ratio | 0.99 |
| Sortino Ratio | 0.117 |
| Maximum Drawdown | -11.25% |
| Win Rate | 56% |
V. FULL PYTHON CODE
import numpy as np
from collections import deque
from AlgorithmImports import *
class VolatilityWeightedShortTermReversal(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2005, 1, 1)
self.SetCash(100000)
self.t_bill = 'BIL'
developed = [
"EWJ", # iShares MSCI Japan Index ETF
"EFNL", # iShares MSCI Finland Capped Investable Market Index ETF
"IVV", # iShares S&P 500 Index
"EWQ", # iShares MSCI France Index ETF
"EWU", # iShares MSCI United Kingdom ETF
"EWI", # iShares MSCI Italy Index ETF
"ENZL", # iShares MSCI New Zealand Investable Market Index Fund
"NORW" # Global X FTSE Norway 30 ETF
"EWY", # iShares MSCI South Korea Index ETF
"EWP", # iShares MSCI Spain Index ETF
"EWD", # iShares MSCI Sweden Index ETF
"EWG", # iShares MSCI Germany Index ETF
"EWL", # iShares MSCI Switzerland Index ETF
"EWC", # iShares MSCI Canada Index ETF
"EWO", # iShares MSCI Austria Investable Mkt Index ETF
"EWK", # iShares MSCI Belgium Investable Market Index ETF
"EWN", # iShares MSCI Netherlands ETF
"EWA", # iShares MSCI-Australia ETF
]
emerging = ['FXI', 'ARGT', 'EZA', 'AND', 'FXI', 'EWH', 'EWT', 'EIDO', 'EPHE', 'EWM', 'THD', 'EWS', 'TUR', 'EWZ', 'ARGT', 'ECH', 'EPOL', 'EWW', 'ERUS', 'EPI', 'EIDO', 'GAF']
reits = ['IYR', 'REM', 'REZ', 'IFEU']
bonds = ["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
"ASX_XT1", # 10 Year Commonwealth Treasury Bond Futures, Continuous Contract #1
"ASX_YT1", # 3 Year Commonwealth Treasury Bond Futures, Continuous Contract #1
"EUREX_FGBL1", # Euro-Bund (10Y) Futures, Continuous Contract #1
"EUREX_FBTP1", # Long-Term Euro-BTP Futures, Continuous Contract #1
"EUREX_FGBM1", # Euro-Bobl Futures, Continuous Contract #1
"EUREX_FGBS1", # Euro-Schatz Futures, Continuous Contract #1
"SGX_JB1", # SGX 10-Year Mini Japanese Government Bond Futures
"LIFFE_R1" # Long Gilt Futures, Continuous Contract #1
"MX_CGB1", # Ten-Year Government of Canada Bond Futures, Continuous Contract #1
]
commodities = [ "CME_S1", # Soybean Futures, Continuous Contract
"CME_W1", # Wheat Futures, Continuous Contract
"CME_SM1", # Soybean Meal Futures, Continuous Contract
"CME_BO1", # Soybean Oil Futures, Continuous Contract
"CME_C1", # Corn Futures, Continuous Contract
"CME_O1", # Oats Futures, Continuous Contract
"CME_LC1", # Live Cattle Futures, Continuous Contract
"CME_FC1", # Feeder Cattle Futures, Continuous Contract
"CME_LN1", # Lean Hog Futures, Continuous Contract
"CME_GC1", # Gold Futures, Continuous Contract
"CME_SI1", # Silver Futures, Continuous Contract
"CME_PL1", # Platinum Futures, Continuous Contract
"CME_CL1", # Crude Oil Futures, Continuous Contract
"CME_HG1", # Copper Futures, Continuous Contract
"CME_LB1", # Random Length Lumber Futures, Continuous Contract
"CME_PA1", # Palladium Futures, Continuous Contract
"CME_RR1", # Rough Rice Futures, Continuous Contract
"ICE_RS1", # Canola Futures, Continuous Contract
"ICE_GO1", # Gas Oil Futures, Continuous Contract
"CME_RB2", # Gasoline Futures, Continuous Contract
"CME_KW2", # Wheat Kansas, Continuous Contract
"ICE_WT1", # WTI Crude Futures, Continuous Contract
"ICE_CC1", # Cocoa Futures, Continuous Contract
"ICE_CT1", # Cotton No. 2 Futures, Continuous Contract
"ICE_KC1", # Coffee C Futures, Continuous Contract
"ICE_O1", # Heating Oil Futures, Continuous Contract
"ICE_OJ1", # Orange Juice Futures, Continuous Contract
"ICE_SB1" # Sugar No. 11 Futures, Continuous Contract
]
self.data = {} # Monthly symbol closes.
self.index_price = {} # Asset class index price.
self.sma = {} # Asset class SMA.
self.period = 12
self.custom_data: List[str] = bonds + commodities
self.asset_classes = {}
self.asset_classes['developed'] = developed
self.asset_classes['emerging'] = emerging
self.asset_classes['reits'] = reits
self.asset_classes['bonds'] = bonds
self.asset_classes['commodities'] = commodities
for symbol in [self.t_bill] + developed + emerging + reits:
self.AddEquity(symbol, Resolution.Daily)
self.data[symbol] = deque(maxlen = self.period)
for symbol in bonds + commodities:
data = self.AddData(QuantpediaFutures, symbol, Resolution.Daily)
self.data[symbol] = deque(maxlen = self.period)
data.SetFeeModel(CustomFeeModel())
#data.SetLeverage(2)
for asset_class in self.asset_classes:
self.sma[asset_class] = SimpleMovingAverage(10)
self.index_price[asset_class] = 0
self.rebalance_flag: bool = False
self.Schedule.On(self.DateRules.MonthStart(emerging[0]), self.TimeRules.AfterMarketOpen(emerging[0]), self.Rebalance)
self.settings.daily_precise_end_time = False
def on_data(self, slice: Slice) -> None:
if not self.rebalance_flag:
return
self.rebalance_flag = False
uptrend_classes = []
downtrend_classes = []
# Calculate index price.
for asset_class in self.asset_classes:
class_symbols = self.asset_classes[asset_class]
class_symbols_count = len(class_symbols)
class_total_price = 0
# Store index price and symbol price.
for symbol in class_symbols:
if symbol in self.custom_data:
if self.securities[symbol].get_last_data() and self.time.date() > QuantpediaFutures.get_last_update_date()[symbol]:
self.liquidate(symbol)
break
if self.Securities.ContainsKey(symbol):
price = self.Securities[symbol].Price
if price != 0:
self.data[symbol].append(price)
class_total_price += price
if class_total_price == 0:
continue
index_price = class_total_price / class_symbols_count
self.index_price[asset_class] = index_price
# Update index SMA.
self.sma[asset_class].Update(self.Time, index_price)
# Trend following filtering.
if self.sma[asset_class].IsReady:
index_price = self.index_price[asset_class]
if index_price > self.sma[asset_class].Current.Value:
uptrend_classes.append(asset_class)
else:
downtrend_classes.append(asset_class)
# Trade execution
self.Liquidate()
class_count = len(self.asset_classes)
targets: List[PortfolioTarget] = []
for asset_class in uptrend_classes:
# Performance calc.
performance = {}
for symbol in self.asset_classes[asset_class]:
if len(self.data[symbol]) == self.data[symbol].maxlen:
closes = np.array([x for x in self.data[symbol]])
daily_retuns = closes[1:] / closes[:-1] - 1
volatility = np.std(daily_retuns) * np.sqrt(252)
ret = closes[-1] / closes[0] - 1
performance[symbol] = ret / volatility
if len(performance) == 0: continue
# Performance sorting.
perf_values = [x for x in performance.values()]
long = [x[0] for x in performance.items() if x[1] >= np.percentile(perf_values, 50)]
for symbol in long:
if symbol in slice and slice[symbol]:
self.SetHoldings(symbol, 1 / (class_count * len(long)))
for asset_class in downtrend_classes:
if symbol in slice and slice[symbol]:
self.SetHoldings(self.t_bill, 1 / class_count)
def Rebalance(self):
self.rebalance_flag = True
# 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"))