Dynamic Momentum Strategy

1from AlgorithmImports import *
2class DynamicMomentumStrategy(QCAlgorithm):
3def Initialize(self):
4 self.SetStartDate(2010, 1, 1)
5 self.SetCash(100000)
6 
7 self.slow_period = 12*21
8 self.fast_period = 21
9 
10 # subscribe 
11 data = self.AddData(QuantpediaFutures, 'CME_ES1', Resolution.Daily)     # E-mini S&P 500 Futures, Continuous Contract #1
12 data.SetFeeModel(CustomFeeModel())
13 data.SetLeverage(5)
14 self.market = data.Symbol
15 
16 # daily price data
17 self.price_data = RollingWindow[float](self.slow_period)
18 self.recent_month:int = -1
19def OnData(self, data):
20 # check if data is still coming.
21 if self.securities[self.market].get_last_data() and self.time.date() > QuantpediaFutures.get_last_update_date()[self.market]:
22     self.liquidate()
23     return
24 # store daily market price
25 if self.market in data and data[self.market]:
26     self.price_data.Add(data[self.market].Value)
27     if self.recent_month != self.Time.month:
28         self.recent_month = self.Time.month
29         
30         if self.price_data.IsReady:
31             slow_momentum = self.price_data[0] / self.price_data[self.price_data.Count-1] - 1
32             fast_momentum = self.price_data[0] / self.price_data[21] - 1
33             
34             slow_signal = 1 if slow_momentum >= 0 else -1
35             fast_signal = 1 if fast_momentum >= 0 else -1
36             
37             # market cycles
38             # A month ending at date t is classified as Bull if both the trailing 12-month return (arithmetic average monthly return), rt−12,t, is nonnegative and
39             # the trailing 1-month return, rt−1,t, is nonnegative. A month is classified as Correction if rt−12,t ≥ 0 but rt−1,t < 0; as Bear if rt−12,t < 0 and rt−1,t < 0; 
40             # and as Rebound if rt−12,t < 0 but rt−1,t ≥ 0. 
41             bull = slow_signal == 1 and fast_signal == 1
42             bear = slow_signal == -1 and fast_signal == -1
43             correction = slow_signal == 1 and fast_signal == -1
44             rebound = slow_signal == -1 and fast_signal == 1
45             
46             alpha = 0
47             
48             # if the market`s state is bear or bull – the alpha is not important since the signals agree and it could be set at one half
49             if bull or bear:
50                 alpha = 0.5
51             # source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3489539
52             # Table 6
53             elif correction:
54                 alpha = 0.16
55             elif rebound:
56                 alpha = 0.69
57             
58             # weight calculation
59             w = ((1-alpha) * slow_signal) + (alpha*fast_signal)
60             self.SetHoldings(self.market, w)
61# Custom fee model.
62class CustomFeeModel(FeeModel):
63def GetOrderFee(self, parameters):
64 fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
65 return OrderFee(CashAmount(fee, "USD"))
66 
67# Quantpedia data.
68# NOTE: IMPORTANT: Data order must be ascending (datewise)
69class QuantpediaFutures(PythonData):
70_last_update_date:Dict[Symbol, datetime.date] = {}
71@staticmethod
72def get_last_update_date() -> Dict[Symbol, datetime.date]:
73return QuantpediaFutures._last_update_date
74def GetSource(self, config, date, isLiveMode):
75 return SubscriptionDataSource("data.quantpedia.com/backtesting_data/futures/{0}.csv".format(config.Symbol.Value), SubscriptionTransportMedium.RemoteFile, FileFormat.Csv)
76def Reader(self, config, line, date, isLiveMode):
77 data = QuantpediaFutures()
78 data.Symbol = config.Symbol
79 
80 if not line[0].isdigit(): return None
81 split = line.split(';')
82 
83 data.Time = datetime.strptime(split[0], "%d.%m.%Y") + timedelta(days=1)
84 data['back_adjusted'] = float(split[1])
85 data['spliced'] = float(split[2])
86 data.Value = float(split[1])
87 if config.Symbol not in QuantpediaFutures._last_update_date:
88     QuantpediaFutures._last_update_date[config.Symbol] = datetime(1,1,1).date()
89 if data.Time.date() > QuantpediaFutures._last_update_date[config.Symbol]:
90     QuantpediaFutures._last_update_date[config.Symbol] = data.Time.date()
91 return data