Time Series Momentum Combined with Volatility Filters in Futures Markets

1import numpy as np
2from AlgorithmImports import *
3from collections import deque
4class TimeSeriesMomentumVolatilityFilters(QCAlgorithm):
5def Initialize(self):
6 self.SetStartDate(2010, 1, 1)
7 self.SetCash(100000)
8 
9 self.symbols = [
10             'ICE_DX1',      # US Dollar Index Futures, Continuous Contract #1
11             'CME_TY1',      # 10 Yr Note Futures, Continuous Contract #1
12             'CME_ES1',      # E-mini S&P 500 Futures, Continuous Contract #1
13             'CME_EC1',      # Euro FX Futures, Continuous Contract #1
14             'CME_BP1',      # British Pound Futures, Continuous Contract #1
15             'CME_HG1'       # Copper Futures, Continuous Contract
16             ]
17 
18 self.period = 12*21
19 self.vol_period = 21
20 self.SetWarmUp(self.period)
21 
22 self.optimalization_count = 100
23 
24 self.data = {}
25 self.macd = {}
26 self.macd_signal = {}
27 self.volatility = {}
28 
29 for symbol in self.symbols:
30     data = self.AddData(QuantpediaFutures, symbol, Resolution.Daily)
31     data.SetFeeModel(CustomFeeModel())
32     
33     self.data[symbol] = deque(maxlen=self.period+self.vol_period+1)
34     self.volatility[symbol] = deque(maxlen=self.period)
35     self.macd_signal[symbol] = deque(maxlen=self.period)
36     
37 self.macd['ICE_DX1'] = self.MACD('ICE_DX1', 1, 250, 1)
38 self.macd['CME_TY1'] = self.MACD('CME_TY1', 1, 250, 1)
39 self.macd['CME_HG1'] = self.MACD('CME_HG1', 1, 250, 1)
40 
41 self.macd['CME_EC1'] = self.MACD('CME_EC1', 1, 61, 1)
42 self.macd['CME_BP1'] = self.MACD('CME_BP1', 1, 61, 1)
43 
44 self.macd['CME_ES1'] = self.MACD('CME_ES1', 3, 250, 1)
45 
46def OnData(self, data):
47 for symbol in self.data:
48     symbol_obj = self.Symbol(symbol)
49     if symbol_obj in data.Keys:
50         if data[symbol_obj]:
51             price = data[symbol_obj].Value
52             if price != 0:
53                 self.data[symbol].append(price)
54 if self.IsWarmingUp: return
55 self.Liquidate()
56 # Optimalization
57 optimal_treshold = {}
58 for symbol in self.symbols:
59     if self.securities[symbol].get_last_data() and self.time.date() > QuantpediaFutures.get_last_update_date()[symbol]:
60         self.liquidate(symbol)
61         continue
62     macd = self.macd[symbol]
63     closes = np.array([x for x in self.data[symbol]])
64     
65     if macd.IsReady and len(closes) >= self.vol_period:
66         # MACD SIGNAL = if short term MA is UNDER long term MA == 1 else -1
67         self.macd_signal[symbol].append(1 if macd.Slow > macd.Fast else -1)
68         daily_return = closes[:-1] / closes[1:] - 1
69         vol = np.std(daily_return) * np.sqrt(252)
70         self.volatility[symbol].append(vol)
71     
72         if len(closes) == self.data[symbol].maxlen and len(self.macd_signal[symbol]) == self.macd_signal[symbol].maxlen:
73             values = np.array(closes)
74             daily_changes = (values[1:] - values[:-1]) / values[:-1]
75             
76             vol_min = min(self.volatility[symbol])
77             vol_max = max(self.volatility[symbol])
78             
79             vol_range = vol_max - vol_min
80             vol_step = vol_range / self.optimalization_count
81             
82             avg_return = {}
83             treshhold_value = vol_min
84             while treshhold_value <= vol_max:
85                 vol_vector = np.array([-1 if x <= treshhold_value else 1 for x in self.volatility[symbol]])
86                 
87                 returns = vol_vector * self.macd_signal[symbol] * daily_changes[-self.period:]
88                 avg_return[treshhold_value] = np.average([x for x in returns])
89                 
90                 treshhold_value += vol_step
91                 
92             optimal_treshold[symbol] = max(avg_return, key=avg_return.get)
93             
94 if len(optimal_treshold) == 0: return
95 
96 # Trading
97 count = len(optimal_treshold)
98 for symbol, threshold in optimal_treshold.items():
99     vol = self.volatility[symbol][-1]
100     
101     if symbol in data and data[symbol]:
102         # low volatility enviroment
103         if vol <= threshold: 
104             if self.macd[symbol].Fast > self.macd[symbol].Slow:
105                 self.SetHoldings(symbol, 1/count)
106             else:
107                 self.SetHoldings(symbol, -1/count)
108         
109         # high volatility enviroment
110         else: 
111             if self.macd[symbol].Fast > self.macd[symbol].Slow:
112                 self.SetHoldings(symbol, -1/count)
113             else:
114                 self.SetHoldings(symbol, 1/count)
115# Quantpedia data.
116# NOTE: IMPORTANT: Data order must be ascending (datewise)
117class QuantpediaFutures(PythonData):
118_last_update_date:Dict[Symbol, datetime.date] = {}
119@staticmethod
120def get_last_update_date() -> Dict[Symbol, datetime.date]:
121return QuantpediaFutures._last_update_date
122def GetSource(self, config, date, isLiveMode):
123 return SubscriptionDataSource("data.quantpedia.com/backtesting_data/futures/{0}.csv".format(config.Symbol.Value), SubscriptionTransportMedium.RemoteFile, FileFormat.Csv)
124def Reader(self, config, line, date, isLiveMode):
125 data = QuantpediaFutures()
126 data.Symbol = config.Symbol
127 
128 if not line[0].isdigit(): return None
129 split = line.split(';')
130 
131 data.Time = datetime.strptime(split[0], "%d.%m.%Y") + timedelta(days=1)
132 data['back_adjusted'] = float(split[1])
133 data['spliced'] = float(split[2])
134 data.Value = float(split[1])
135 if config.Symbol.Value not in QuantpediaFutures._last_update_date:
136     QuantpediaFutures._last_update_date[config.Symbol.Value] = datetime(1,1,1).date()
137 if data.Time.date() > QuantpediaFutures._last_update_date[config.Symbol.Value]:
138     QuantpediaFutures._last_update_date[config.Symbol.Value] = data.Time.date()
139 return data
140# Custom fee model.
141class CustomFeeModel(FeeModel):
142def GetOrderFee(self, parameters):
143 fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
144 return OrderFee(CashAmount(fee, "USD"))