Trendfollowing Combined with Momentum in Commodity Futures

1import numpy as np
2from AlgorithmImports import *
3class TrendfollowingwithMomentum(QCAlgorithm):
4def Initialize(self):
5 self.SetStartDate(1991, 1, 1)
6 self.SetCash(100000)
7 self.symbols = [
8     "CME_S1",   # Soybean Futures, Continuous Contract
9     "CME_W1",   # Wheat Futures, Continuous Contract
10     "CME_SM1",  # Soybean Meal Futures, Continuous Contract
11     "CME_BO1",  # Soybean Oil Futures, Continuous Contract
12     "CME_C1",   # Corn Futures, Continuous Contract
13     "CME_O1",   # Oats Futures, Continuous Contract
14     "CME_LC1",  # Live Cattle Futures, Continuous Contract
15     "CME_FC1",  # Feeder Cattle Futures, Continuous Contract
16     "CME_LN1",  # Lean Hog Futures, Continuous Contract
17     "CME_GC1",  # Gold Futures, Continuous Contract
18     "CME_SI1",  # Silver Futures, Continuous Contract
19     "CME_PL1",  # Platinum Futures, Continuous Contract
20     "CME_CL1",  # Crude Oil Futures, Continuous Contract
21     "CME_HG1",  # Copper Futures, Continuous Contract
22     "CME_LB1",  # Random Length Lumber Futures, Continuous Contract
23     # "CME_NG1",  # Natural Gas (Henry Hub) Physical Futures, Continuous Contract
24     "CME_PA1",  # Palladium Futures, Continuous Contract 
25     "CME_RR1",  # Rough Rice Futures, Continuous Contract
26     "CME_DA1",  # Class III Milk Futures
27     "ICE_RS1",  # Canola Futures, Continuous Contract
28     "ICE_GO1",  # Gas Oil Futures, Continuous Contract
29     "CME_RB2",  # Gasoline Futures, Continuous Contract
30     "CME_KW2",  # Wheat Kansas, Continuous Contract
31     "ICE_WT1",  # WTI Crude Futures, Continuous Contract
32     "ICE_CC1",  # Cocoa Futures, Continuous Contract 
33     "ICE_CT1",  # Cotton No. 2 Futures, Continuous Contract
34     "ICE_KC1",  # Coffee C Futures, Continuous Contract
35     "ICE_O1",   # Heating Oil Futures, Continuous Contract
36     "ICE_OJ1",  # Orange Juice Futures, Continuous Contract
37     "ICE_SB1",  # Sugar No. 11 Futures, Continuous Contract
38 ]
39 self.winners = []
40 self.losers = []
41 self.data = {}
42 self.period = 12*21
43 self.SetWarmUp(self.period)
44 
45 for symbol in self.symbols:
46     data = self.AddData(QuantpediaFutures, symbol, Resolution.Daily)
47     data.SetLeverage(10)
48     data.SetFeeModel(CustomFeeModel())
49     
50     ma = self.SMA(symbol, 6*21, Resolution.Daily)
51     self.data[symbol] = SymbolData(symbol, 60, self.period, ma)
52 
53 self.rebalance_flag: bool = False
54 self.Schedule.On(self.DateRules.MonthEnd(self.symbols[0]), self.TimeRules.At(0, 0), self.Rebalance)
55
56def OnData(self, data):
57 for symbol, symbol_data in self.data.items():
58     if self.securities[symbol].get_last_data() and self.time.date() > QuantpediaFutures.get_last_update_date()[symbol]:
59         self.liquidate(symbol)
60         symbol_data.History.reset()
61         continue
62     symbol_obj = self.Symbol(symbol)
63     if symbol_obj in data.Keys:
64         if data[symbol_obj]:
65             price = data[symbol_obj].Value
66             if price != 0:
67                 self.data[symbol].Update(price)
68 
69 if not self.rebalance_flag:
70     return
71 self.rebalance_flag = False
72 targets: List[PortfolioTarget] = []
73 for i, portfolio in enumerate([self.winners, self.losers]):
74     for symbol_data in portfolio:
75         if symbol_data[1].Weight != 0:
76             if symbol_data[0] in data and data[symbol_data[0]]:
77                 targets.append(PortfolioTarget(symbol_data[0], ((-1) ** i) * symbol_data[1].Weight))
78 self.SetHoldings(targets, True)
79 self.winners.clear()
80 self.losers.clear()
81def Rebalance(self):
82 # Return sorting
83 return_values = []
84 for data in self.data.items():
85     if data[1].IsReady():
86         return_values.append(data[1].Return())
87 
88 if len(return_values) == 0: return
89
90 high_percentile = np.percentile(return_values, 75)
91 low_percentile = np.percentile(return_values, 25)
92 winners_by_ret = list(data for data in self.data.items() if data[1].IsReady() and data[1].Return() > high_percentile)
93 losers_by_ret = list(data for data in self.data.items() if data[1].IsReady() and data[1].Return() < low_percentile)
94 
95 # Weighting
96 total_vol = sum((1.0/data[1].Volatility()) for data in winners_by_ret if data[1].IsReady()) + sum((1.0/data[1].Volatility()) for data in losers_by_ret if data[1].IsReady())
97 for data in winners_by_ret + losers_by_ret:
98     if data[1].IsReady():
99         vol = data[1].Volatility()
100         data[1].Weight = (1.0 / vol) / total_vol
101 # Trend sorting
102 self.winners = list(data for data in winners_by_ret if data[1].Price > data[1].MA.Current.Value)
103 self.losers = list(data for data in losers_by_ret if data[1].Price < data[1].MA.Current.Value)
104 self.rebalance_flag = True
105class SymbolData:
106def __init__(self, symbol, volatility_lookback, return_lookback, ma):
107 self.Symbol = symbol
108 self.History = RollingWindow[float](return_lookback)
109 self.Price = 0.0
110 self.MA = ma
111 self.Weight = 0.0
112 self.Volatility_lookback = volatility_lookback
113def IsReady(self) -> bool:
114 return self.History.IsReady
115     
116def Update(self, value: float):
117 self.Price = value
118 self.History.Add(float(value))
119
120def Return(self) -> float:
121 prices = [x for x in self.History]
122 return prices[0] / prices[-1] - 1
123
124def Volatility(self) -> float:
125 prices = np.array([x for x in self.History])[-self.Volatility_lookback:]
126 returns = prices[:-1] / prices[1:] - 1
127 return np.std(returns) * np.sqrt(252)
128
129# Quantpedia data.
130# NOTE: IMPORTANT: Data order must be ascending (datewise)
131class QuantpediaFutures(PythonData):
132_last_update_date:Dict[Symbol, datetime.date] = {}
133@staticmethod
134def get_last_update_date() -> Dict[Symbol, datetime.date]:
135return QuantpediaFutures._last_update_date
136def GetSource(self, config, date, isLiveMode):
137 return SubscriptionDataSource("data.quantpedia.com/backtesting_data/futures/{0}.csv".format(config.Symbol.Value), SubscriptionTransportMedium.RemoteFile, FileFormat.Csv)
138def Reader(self, config, line, date, isLiveMode):
139 data = QuantpediaFutures()
140 data.Symbol = config.Symbol
141 
142 if not line[0].isdigit(): return None
143 split = line.split(';')
144 
145 data.Time = datetime.strptime(split[0], "%d.%m.%Y") + timedelta(days=1)
146 data['back_adjusted'] = float(split[1])
147 data['spliced'] = float(split[2])
148 data.Value = float(split[1])
149 if config.Symbol.Value not in QuantpediaFutures._last_update_date:
150     QuantpediaFutures._last_update_date[config.Symbol.Value] = datetime(1,1,1).date()
151 if data.Time.date() > QuantpediaFutures._last_update_date[config.Symbol.Value]:
152     QuantpediaFutures._last_update_date[config.Symbol.Value] = data.Time.date()
153 return data
154# Custom fee model.
155class CustomFeeModel(FeeModel):
156def GetOrderFee(self, parameters):
157 fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
158 return OrderFee(CashAmount(fee, "USD"))