The strategy trades Chinese commodity futures using a price-based signal, forming a long-short portfolio, rebalancing monthly, employing Gradual Rolling, and excluding low-liquidity commodities for real-world investability.
ASSET CLASS: futures | REGION: China | FREQUENCY:
Monthly | MARKET: commodities | KEYWORD: Carry, China
I. STRATEGY IN A NUTSHELL
The strategy trades Chinese commodity futures using 12-month basis-momentum, forming long-short portfolios, rebalancing monthly, and gradually rolling contracts to optimize liquidity and momentum capture.
II. ECONOMIC RATIONALE
Basis-momentum reflects speculators’ and intermediaries’ market-clearing roles, with term structure and rolling methods enhancing predictability, liquidity management, and capacity-adjusted returns in commodity futures 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 | 8.07% |
| Volatility | 9.94% |
| Beta | -0.016 |
| Sharpe Ratio | 0.81 |
| Sortino Ratio | N/A |
| Maximum Drawdown | -22.81% |
| Win Rate | 50% |
V. FULL PYTHON CODE
from AlgorithmImports import *
#endregion
class CarryCommodityPremiainChina(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.contract_range:list[int] = [2, 3] # 2nd and 3rd futures contract
self.latest_update_date:dict = {} # latest price data arrival time
for symbol in self.symbols:
# futures data
for i in self.contract_range:
sym = symbol + str(i)
data = self.AddData(QuantpediaChineseFutures, sym, Resolution.Daily)
data.SetLeverage(5)
data.SetFeeModel(CustomFeeModel())
self.latest_update_date[symbol] = None
self.recent_month = -1
def OnData(self, data):
signal:dict[Symbol, float] = {}
# store daily prices
for symbol in self.symbols:
# both contracts data points are available
if all(symbol+str(i) in data and data[symbol+str(i)] and data[symbol+str(i)].Value != 0 for i in self.contract_range):
near_c_symbol:str = symbol + str(self.contract_range[0]) # 2nd contract
dist_c_symbol:str = symbol + str(self.contract_range[1]) # 3rd contract
self.latest_update_date[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[symbol]).days > 5:
continue
# calculate signal from spliced price contract data
signal[dist_c_symbol] = data[near_c_symbol].GetProperty('close') / data[dist_c_symbol].GetProperty('close') - 1
# monthly rebalance
if self.Time.month == self.recent_month:
return
self.recent_month = self.Time.month
# buying the commodity futures with positive signal and selling commodity futures with a negative signal
long:list[Symbol] = [x[0] for x in signal.items() if x[1] > 0.]
short:list[Symbol] = [x[0] for x in signal.items() if x[1] < 0.]
# 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:
if symbol in data and data[symbol]:
self.SetHoldings(symbol, 1 / len(long))
for symbol in short:
if symbol in data and data[symbol]:
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
