Commodity · Global · futures · Monthly · Global Free

Trading Commodity Calendar Spreads

Academic paper

AuthorsNikkanen

Paper linkhttp://herkules.oulu.fi/thesis/nbnfioulu-201211141056.pdf

Strategy in a nutshell

This strategy trades 20 commodity futures by analyzing the first 12 months of the futures curve. Positions are long in backwardated contracts and short in contangoed contracts, or vice versa, with the portfolio equally weighted and rebalanced monthly.

Economic rationale

The strategy exploits roll yield, a primary driver of commodity returns. By taking offsetting positions along the curve, it captures returns from backwardation and contango while reducing overall portfolio volatility.

Backtest performance

Annualised return6.21%

Volatility2.01%

Beta0.002

Sharpe ratio3.09

Win rate51%

Full Python code

1import numpy as np
2class TradingCommodityCalendarSpreads(QCAlgorithm):
3
4def Initialize(self):
5self.SetStartDate(2010, 1, 1)
6self.SetCash(100000)
7   
8# 1st contract and the number of contracts to approximately 12 months away.
9self.contracts = {
10                "CHRIS/CME_S" :  8,  # Soybean Futures, Continuous Contract
11                "CHRIS/CME_W"  : 6,  # Wheat Futures, Continuous Contract
12                "CHRIS/CME_SM" : 9,  # Soybean Meal Futures, Continuous Contract
13                "CHRIS/CME_BO" : 9,  # Soybean Oil Futures, Continuous Contract
14                "CHRIS/CME_C" :  6,  # Corn Futures, Continuous Contract
15                "CHRIS/CME_O" :  6,  # Oats Futures, Continuous Contract
16                "CHRIS/CME_LC" : 7,  # Live Cattle Futures, Continuous Contract
17                "CHRIS/CME_FC" : 7,  # Feeder Cattle Futures, Continuous Contract
18                "CHRIS/CME_LN" : 9,  # Lean Hog Futures, Continuous Contract
19                "CHRIS/CME_GC" : 9,  # Gold Futures, Continuous Contract
20                "CHRIS/CME_SI" : 9,  # Silver Futures, Continuous Contract
21                "CHRIS/CME_PL" : 7,  # Platinum Futures, Continuous Contract
22                "CHRIS/CME_CL" : 12, # Crude Oil Futures, Continuous Contract
23                "CHRIS/CME_HG" : 13, # Copper Futures, Continuous Contract
24                "CHRIS/CME_LB" : 7,  # Random Length Lumber Futures, Continuous Contract
25                "CHRIS/CME_NG" : 12, # Natural Gas (Henry Hub) Physical Futures, Continuous Contract
26                "CHRIS/CME_PA" : 7,  # Palladium Futures, Continuous Contract 
27                "CHRIS/CME_RR" : 7,  # Rough Rice Futures, Continuous Contract
28                # "CHRIS/CME_CU" : 13, # Chicago Ethanol (Platts) Futures
29                "CHRIS/CME_DA" : 13, # Class III Milk Futures
30                
31                "CHRIS/ICE_CC" : 6,  # Cocoa Futures, Continuous Contract 
32                "CHRIS/ICE_CT" : 6,  # Cotton No. 2 Futures, Continuous Contract
33                "CHRIS/ICE_KC" : 6,  # Coffee C Futures, Continuous Contract
34                "CHRIS/ICE_O" :  13, # Heating Oil Futures, Continuous Contract
35                "CHRIS/ICE_OJ" : 7,  # Orange Juice Futures, Continuous Contract
36                "CHRIS/ICE_SB" : 5   # Sugar No. 11 Futures, Continuous Contract
37                }
38self.rebalance_flag = True
39
40for future, future_count in self.contracts.items():
41    for index in range(1, future_count + 1):
42        contract = future + str(index)
43
44        data = self.AddData(QuandlFutures, contract, Resolution.Daily)
45        data.SetFeeModel(CustomFeeModel(self))
46        data.SetLeverage(5)
47
48self.Schedule.On(self.DateRules.MonthStart('CHRIS/CME_S1'), self.TimeRules.AfterMarketOpen('CHRIS/CME_S1'), self.Rebalance)
49
50def Rebalance(self):
51self.rebalance_flag = True
52
53def OnData(self, data):
54if not self.rebalance_flag:
55    return
56self.rebalance_flag = False
57
58long = []
59short = []
60for future, future_count in self.contracts.items():
61    # curve_shape = sum( np.diff( [ self.Securities[future + str(index)].Price for index in range(1, 6) if self.Securities.ContainsKey(future + str(index)) ] ) )
62    curve_shape = sum( np.diff( [ data[future + str(index)].Value for index in range(1, 6) if (future + str(index)) in data ] ) )
63    curve_shape /= future_count
64    if curve_shape != 0:
65        # diff = np.diff( [ self.Securities[future + str(index)].Price for index in range(1, future_count + 1) if self.Securities.ContainsKey(future + str(index)) ] )
66        diff = np.diff( [ data[future + str(index)].Value for index in range(1, future_count + 1) if (future + str(index)) in data ] )
67        abs_diff = [abs(x) for x in diff]
68        max_diff_index = abs_diff.index(max(abs_diff))
69        min_diff_index = abs_diff.index(min(abs_diff))
70        
71        # Offset index by 2 to get right symbols to trade.
72        max_diff_index += 2
73        min_diff_index += 2
74        
75        if curve_shape > 0:
76            # Backwardation.
77            long.append(future + str(max_diff_index))
78            short.append(future + str(min_diff_index))
79        else:
80            # Contango.
81            long.append(future + str(min_diff_index))
82            short.append(future + str(max_diff_index))
83
84# Trade execution.
85invested = [x.Key.Value for x in self.Portfolio if x.Value.Invested]
86for symbol in invested:
87    if symbol not in long + short:
88        self.Liquidate(symbol)
89
90weight = 1 / len(self.contracts)
91
92for symbol in long:
93    if self.Securities[symbol].Price != 0:
94        self.SetHoldings(symbol, weight)
95for symbol in short:
96    if self.Securities[symbol].Price != 0:
97        self.SetHoldings(symbol, -weight)
98# Quandl free data
99class QuandlFutures(PythonQuandl):
100def __init__(self):
101self.ValueColumnName = "settle"
102# Custom fee model.
103class CustomFeeModel(FeeModel):
104def GetOrderFee(self, parameters):
105fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
106return OrderFee(CashAmount(fee, "USD"))