Momentum and Trend Following in Global Asset Allocation

1import numpy as np
2from collections import deque
3from AlgorithmImports import *
4class VolatilityWeightedShortTermReversal(QCAlgorithm):
5def Initialize(self):
6 self.SetStartDate(2005, 1, 1)
7 self.SetCash(100000)
8 self.t_bill = 'BIL'
9 
10 developed = [
11     "EWJ", # iShares MSCI Japan Index ETF
12     "EFNL", # iShares MSCI Finland Capped Investable Market Index ETF
13     "IVV",  # iShares S&P 500 Index
14     "EWQ",  # iShares MSCI France Index ETF
15     "EWU",  # iShares MSCI United Kingdom ETF
16     "EWI",  # iShares MSCI Italy Index ETF
17     "ENZL", # iShares MSCI New Zealand Investable Market Index Fund
18     "NORW"  # Global X FTSE Norway 30 ETF
19     "EWY",  # iShares MSCI South Korea Index ETF
20     "EWP",  # iShares MSCI Spain Index ETF
21     "EWD",  # iShares MSCI Sweden Index ETF
22     "EWG",  # iShares MSCI Germany Index ETF
23     "EWL",  # iShares MSCI Switzerland Index ETF
24     "EWC",  # iShares MSCI Canada Index ETF
25     "EWO",  # iShares MSCI Austria Investable Mkt Index ETF
26     "EWK",  # iShares MSCI Belgium Investable Market Index ETF
27     "EWN",  # iShares MSCI Netherlands ETF
28     "EWA",  # iShares MSCI-Australia ETF
29 ]
30 
31 emerging = ['FXI', 'ARGT', 'EZA', 'AND', 'FXI', 'EWH', 'EWT', 'EIDO', 'EPHE', 'EWM', 'THD', 'EWS', 'TUR', 'EWZ', 'ARGT', 'ECH', 'EPOL', 'EWW', 'ERUS', 'EPI', 'EIDO', 'GAF']
32 
33 reits = ['IYR', 'REM', 'REZ', 'IFEU']
34 bonds = ["CME_TY1",     # 10 Yr Note Futures, Continuous Contract #1
35         "CME_FV1",      # 5 Yr Note Futures, Continuous Contract #1
36         "CME_TU1",      # 2 Yr Note Futures, Continuous Contract #1
37         "ASX_XT1",      # 10 Year Commonwealth Treasury Bond Futures, Continuous Contract #1
38         "ASX_YT1",      # 3 Year Commonwealth Treasury Bond Futures, Continuous Contract #1
39         "EUREX_FGBL1",  # Euro-Bund (10Y) Futures, Continuous Contract #1
40         "EUREX_FBTP1",  # Long-Term Euro-BTP Futures, Continuous Contract #1
41         "EUREX_FGBM1",  # Euro-Bobl Futures, Continuous Contract #1
42         "EUREX_FGBS1",  # Euro-Schatz Futures, Continuous Contract #1 
43         "SGX_JB1",      # SGX 10-Year Mini Japanese Government Bond Futures
44         "LIFFE_R1"      # Long Gilt Futures, Continuous Contract #1
45         "MX_CGB1",      # Ten-Year Government of Canada Bond Futures, Continuous Contract #1
46         ]
47         
48 commodities = [ "CME_S1",   # Soybean Futures, Continuous Contract
49                 "CME_W1",   # Wheat Futures, Continuous Contract
50                 "CME_SM1",  # Soybean Meal Futures, Continuous Contract
51                 "CME_BO1",  # Soybean Oil Futures, Continuous Contract
52                 "CME_C1",   # Corn Futures, Continuous Contract
53                 "CME_O1",   # Oats Futures, Continuous Contract
54                 "CME_LC1",  # Live Cattle Futures, Continuous Contract
55                 "CME_FC1",  # Feeder Cattle Futures, Continuous Contract
56                 "CME_LN1",  # Lean Hog Futures, Continuous Contract
57                 "CME_GC1",  # Gold Futures, Continuous Contract
58                 "CME_SI1",  # Silver Futures, Continuous Contract
59                 "CME_PL1",  # Platinum Futures, Continuous Contract
60                 "CME_CL1",  # Crude Oil Futures, Continuous Contract
61                 "CME_HG1",  # Copper Futures, Continuous Contract
62                 "CME_LB1",  # Random Length Lumber Futures, Continuous Contract
63                 "CME_PA1",  # Palladium Futures, Continuous Contract 
64                 "CME_RR1",  # Rough Rice Futures, Continuous Contract
65                 "ICE_RS1",  # Canola Futures, Continuous Contract
66                 "ICE_GO1",  # Gas Oil Futures, Continuous Contract
67                 "CME_RB2",  # Gasoline Futures, Continuous Contract
68                 "CME_KW2",  # Wheat Kansas, Continuous Contract
69                 "ICE_WT1",  # WTI Crude Futures, Continuous Contract
70                 
71                 "ICE_CC1",  # Cocoa Futures, Continuous Contract 
72                 "ICE_CT1",  # Cotton No. 2 Futures, Continuous Contract
73                 "ICE_KC1",  # Coffee C Futures, Continuous Contract
74                 "ICE_O1",   # Heating Oil Futures, Continuous Contract
75                 "ICE_OJ1",  # Orange Juice Futures, Continuous Contract
76                 "ICE_SB1"   # Sugar No. 11 Futures, Continuous Contract
77             ]
78 
79 self.data = {}  # Monthly symbol closes.
80 
81 self.index_price = {} # Asset class index price.
82 self.sma = {}   # Asset class SMA.
83 self.period = 12
84 
85 self.custom_data: List[str] = bonds + commodities
86 self.asset_classes = {}
87 self.asset_classes['developed'] = developed
88 self.asset_classes['emerging'] = emerging
89 self.asset_classes['reits'] = reits
90 self.asset_classes['bonds'] = bonds
91 self.asset_classes['commodities'] = commodities
92 for symbol in [self.t_bill] + developed + emerging + reits:
93     self.AddEquity(symbol, Resolution.Daily)
94     self.data[symbol] = deque(maxlen = self.period)
95 
96 for symbol in bonds + commodities:
97     data = self.AddData(QuantpediaFutures, symbol, Resolution.Daily)
98     self.data[symbol] = deque(maxlen = self.period)
99     data.SetFeeModel(CustomFeeModel())
100     #data.SetLeverage(2)
101 
102 for asset_class in self.asset_classes:
103     self.sma[asset_class] = SimpleMovingAverage(10)
104     self.index_price[asset_class] = 0
105     
106 self.rebalance_flag: bool = False
107 self.Schedule.On(self.DateRules.MonthStart(emerging[0]), self.TimeRules.AfterMarketOpen(emerging[0]), self.Rebalance)
108 self.settings.daily_precise_end_time = False
109def on_data(self, slice: Slice) -> None:
110 if not self.rebalance_flag:
111     return
112 self.rebalance_flag = False
113 uptrend_classes = []
114 downtrend_classes = []
115 
116 # Calculate index price.
117 for asset_class in self.asset_classes:
118     
119     class_symbols = self.asset_classes[asset_class]
120     class_symbols_count = len(class_symbols)
121     class_total_price = 0
122     
123     # Store index price and symbol price.
124     for symbol in class_symbols:
125         if symbol in self.custom_data:
126             if self.securities[symbol].get_last_data() and self.time.date() > QuantpediaFutures.get_last_update_date()[symbol]:
127                 self.liquidate(symbol)
128                 break
129         if self.Securities.ContainsKey(symbol):
130             price = self.Securities[symbol].Price
131             if price != 0:
132                 self.data[symbol].append(price)
133                 class_total_price += price
134     
135     if class_total_price == 0:
136         continue
137     index_price = class_total_price / class_symbols_count
138     self.index_price[asset_class] = index_price
139     
140     # Update index SMA.
141     self.sma[asset_class].Update(self.Time, index_price)
142     
143     # Trend following filtering.
144     if self.sma[asset_class].IsReady:
145         index_price = self.index_price[asset_class]
146         
147         if index_price > self.sma[asset_class].Current.Value:
148             uptrend_classes.append(asset_class)
149         else:
150             downtrend_classes.append(asset_class)
151             
152 # Trade execution
153 self.Liquidate()
154 class_count = len(self.asset_classes)
155 
156 targets: List[PortfolioTarget] = []
157 for asset_class in uptrend_classes:
158     # Performance calc.
159     performance = {}
160     for symbol in self.asset_classes[asset_class]:
161         if len(self.data[symbol]) == self.data[symbol].maxlen:
162             closes = np.array([x for x in self.data[symbol]])
163             daily_retuns = closes[1:] / closes[:-1] - 1
164             volatility = np.std(daily_retuns) * np.sqrt(252)
165             ret = closes[-1] / closes[0] - 1
166             performance[symbol] = ret / volatility
167     
168     if len(performance) == 0: continue
169     
170     # Performance sorting.
171     perf_values = [x for x in performance.values()]
172     long = [x[0] for x in performance.items() if x[1] >= np.percentile(perf_values, 50)]
173     for symbol in long:
174         if symbol in slice and slice[symbol]:
175             self.SetHoldings(symbol, 1 / (class_count * len(long)))
176 
177 for asset_class in downtrend_classes:
178     if symbol in slice and slice[symbol]:
179         self.SetHoldings(self.t_bill, 1 / class_count)
180def Rebalance(self):
181 self.rebalance_flag = True
182# Quantpedia data.
183# NOTE: IMPORTANT: Data order must be ascending (datewise)
184class QuantpediaFutures(PythonData):
185_last_update_date:Dict[Symbol, datetime.date] = {}
186@staticmethod
187def get_last_update_date() -> Dict[Symbol, datetime.date]:
188return QuantpediaFutures._last_update_date
189def GetSource(self, config, date, isLiveMode):
190 return SubscriptionDataSource("data.quantpedia.com/backtesting_data/futures/{0}.csv".format(config.Symbol.Value), SubscriptionTransportMedium.RemoteFile, FileFormat.Csv)
191def Reader(self, config, line, date, isLiveMode):
192 data = QuantpediaFutures()
193 data.Symbol = config.Symbol
194 
195 if not line[0].isdigit(): return None
196 split = line.split(';')
197 
198 data.Time = datetime.strptime(split[0], "%d.%m.%Y") + timedelta(days=1)
199 data['back_adjusted'] = float(split[1])
200 data['spliced'] = float(split[2])
201 data.Value = float(split[1])
202 if config.Symbol.Value not in QuantpediaFutures._last_update_date:
203     QuantpediaFutures._last_update_date[config.Symbol.Value] = datetime(1,1,1).date()
204 if data.Time.date() > QuantpediaFutures._last_update_date[config.Symbol.Value]:
205     QuantpediaFutures._last_update_date[config.Symbol.Value] = data.Time.date()
206 return data
207 
208# Custom fee model.
209class CustomFeeModel(FeeModel):
210def GetOrderFee(self, parameters):
211 fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
212 return OrderFee(CashAmount(fee, "USD"))