The strategy trades Chinese commodity futures, forming a long-short portfolio based on 12-month returns, rebalanced monthly, employing Gradual Rolling, and excluding low-volume contracts for practical investability.
ASSET CLASS: futures | REGION: China | FREQUENCY:
Monthly | MARKET: commodities | KEYWORD: Momentum, China
I. STRATEGY IN A NUTSHELL
The strategy trades Chinese commodity futures using 12-month cross-sectional momentum, forming long-short portfolios, applying gradual contract rolls, and rebalancing monthly for real-world liquidity.
II. ECONOMIC RATIONALE
Momentum arises from behavioral biases and macroeconomic risks, with rolling methods managing liquidity and capacity, enabling effective long-short futures strategies in China’s commodity markets.
III. SOURCE PAPER
Investable Commodity Premia in China [Click to Open PDF]
Robert J. Bianchi, Griffith University; John Hua Fan, Griffith University – Department of Accounting, Finance and Economics; Tingxi Zhang, Curtin University
<Abstract>
We investigate the investability of commodity risk premia in China. Previously documented standard momentum, carry and basis-momentum factors are not investable due to the unique liquidity patterns along the futures curves in China. However, dynamic rolling and strategic portfolio weights significantly boost the investment capacity of such premia without compromising its statistical and economic significance. Meanwhile, style integration delivers enhanced performance and improved opportunity sets. Furthermore, the observed investable premia are robust to execution lags, stop-loss, illiquidity, sub-period specifications, seasonality and transaction costs. They also offer portfolio diversification for investors. Finally, investable commodity premia in China reveal strong predictive ability with global real economic growth.
IV. BACKTEST PERFORMANCE
| Annualised Return | 8.99% |
| Volatility | 12.05% |
| Beta | -0.023 |
| Sharpe Ratio | 0.74 |
| Sortino Ratio | N/A |
| Maximum Drawdown | -32.21% |
| Win Rate | 48% |
V. FULL PYTHON CODE
from AlgorithmImports import *
#endregion
class MomentumCommodityPremiainChina(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2017, 1, 1)
self.SetCash(100000)
# NOTE: QC max cap of 100 custom symbols added => 50 commodities x2 contracts
self.symbols:list[str] = [
# 'ER','ME','RO','S','TC','WS','WT', # empty
'CU', 'A', 'AG', 'AL', 'AP', 'AU',
'B', 'BB', 'BU', 'C', 'CF', 'CS',
'CY', 'FB', 'FG', 'FU', 'HC', 'I',
'IC', 'IF', 'IH', 'J', 'JD', 'JM',
'JR', 'L', 'LR', 'M', 'MA', 'NI',
'OI', 'P', 'PB', 'PM', 'PP', 'RB',
'RI', 'RM', 'RS', 'RU', 'SF', 'SM',
'SN', 'SR', 'T', 'TF', 'V',
'WR', 'ZN', 'Y'
# 'ZC', 'TA', 'WH'
]
self.period:int = 12 * 21
self.SetWarmup(self.period, Resolution.Daily)
self.price_data:dict = {}
self.latest_update_date:dict = {} # latest price data arrival time
self.quantile:int = 2
for symbol in self.symbols:
# futures data
sym:str = symbol + '3' # erd contract
data = self.AddData(QuantpediaChineseFutures, sym, Resolution.Daily)
data.SetLeverage(5)
data.SetFeeModel(CustomFeeModel())
self.price_data[sym] = RollingWindow[float](self.period)
self.latest_update_date[sym] = None
self.recent_month = -1
def OnData(self, data):
momentum:dict[Symbol, float] = {}
# store daily prices
for near_c_symbol, _ in self.price_data.items():
# store price data
if near_c_symbol in data and data[near_c_symbol] and data[near_c_symbol].Value != 0:
self.price_data[near_c_symbol].Add(data[near_c_symbol].Value) # 3rd contract price
self.latest_update_date[near_c_symbol] = self.Time.date()
if self.IsWarmingUp: continue
# rebalance date
if self.Time.month != self.recent_month:
# check data arrival time
if (self.Time.date() - self.latest_update_date[near_c_symbol]).days > 5:
continue
# price data for both contracts are ready
if self.price_data[near_c_symbol].IsReady:
# calculate momentum from forward ratio rolled contracts
near_momentum:float = self.price_data[near_c_symbol][0] / self.price_data[near_c_symbol][self.period-1] - 1
momentum[near_c_symbol] = near_momentum
# monthly rebalance
if self.Time.month == self.recent_month:
return
self.recent_month = self.Time.month
long:list[Symbol] = []
short:list[Symbol] = []
if len(momentum) >= self.quantile:
# sort by momentum
sorted_by_momentum = sorted(momentum.items(), key=lambda x: x[1], reverse=True)
quantile:int = int(len(momentum) / self.quantile)
# buying (selling) the half with the highest (lowest) return
long = [x[0] for x in sorted_by_momentum][:quantile]
short = [x[0] for x in sorted_by_momentum][-quantile:]
# trade execution
invested = [x.Key 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 QuantpediaChineseFutures(PythonData):
def GetSource(self, config, date, isLiveMode):
return SubscriptionDataSource("data.quantpedia.com/backtesting_data/futures/china/forward_ratio_rolled/{0}.csv".format(config.Symbol.Value), SubscriptionTransportMedium.RemoteFile, FileFormat.Csv)
def Reader(self, config, line, date, isLiveMode):
data = QuantpediaChineseFutures()
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]) if split[1] != '' else 0 # unadjusted close
data['adj_close'] = float(split[2]) if split[2] != '' else 0
data['last_trade_month'] = int(split[3])
data.Value = float(split[2]) if split[2] != '' else 0
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
