Trend-Following and Spillover Effect

1from AlgorithmImports import *
2import numpy as np
3class TrendFollowingandSpilloverEffect(QCAlgorithm):
4def Initialize(self):
5 self.SetStartDate(2000, 1, 1)
6 self.SetCash(100000)
7 
8 # Symbols - currency, index and bond futures.
9 self.symbols = [
10     ('CME_AD1', 'ASX_YAP1', 'ASX_XT1'),          # Australian Dollar Futures, Continuous Contract #1
11     ('CME_BP1', 'LIFFE_Z1', 'LIFFE_R1'),         # British Pound Futures, Continuous Contract #1
12     ('CME_CD1', 'LIFFE_FCE1', 'MX_CGB1'),        # Canadian Dollar Futures, Continuous Contract #1
13     ('CME_EC1', 'EUREX_FSTX1', 'EUREX_FGBL1'),   # Euro FX Futures, Continuous Contract #1
14     ('CME_JY1', 'SGX_NK1', 'SGX_JB1'),           # Japanese Yen Futures, Continuous Contract #1
15     ('CME_DX1', 'CME_ES1', 'CME_TY1')           # US Dollar Index Futures, Continuous Contract #1
16     # ('CME_SF1', 'EUREX_FSMI1', '')            # Swiss Franc Futures, Continuous Contract #1
17     # ('CME_MP1', '', '')                       # Mexican Peso Futures, Continuous Contract #1
18     # ('CME_NE1', '', '')                       # New Zealand Dollar Futures, Continuous Contract #
19 ]
20 self.symbol = self.AddEquity('SPY', Resolution.Daily).Symbol
21 
22 # Daily ROC data.
23 self.data = {}
24
25 self.period = 36 * 21
26 self.SetWarmUp(self.period)
27 
28 for futures_symbols in self.symbols:
29     for symbol in futures_symbols:
30         data = self.AddData(QuantpediaFutures, symbol, Resolution.Daily)
31         
32         self.data[symbol] = SymbolData(self.period)
33         
34         data.SetFeeModel(CustomFeeModel())
35         data.SetLeverage(5)
36     
37 self.rebalance_flag: bool = False
38 self.Schedule.On(self.DateRules.WeekStart(self.symbol), self.TimeRules.AfterMarketOpen(self.symbol), self.Rebalance)
39 self.settings.daily_precise_end_time = False
40 self.settings.minimum_order_margin_portfolio_percentage = 0.
41
42def OnData(self, data):
43 for futures_symbols in self.symbols:
44     for symbol in futures_symbols:
45         if self.securities[symbol].get_last_data() and self.time.date() > QuantpediaFutures.get_last_update_date()[symbol]:
46             self.liquidate(symbol)
47             self.data[symbol].reset()
48             continue
49         if symbol in data and data[symbol]:
50             price = data[symbol].Value
51             self.data[symbol].update(price)
52 if not self.rebalance_flag:
53     return
54 self.rebalance_flag = False
55 weight = {}
56 traded_asset_classs_count = 0
57 
58 for futures_symbols in self.symbols:
59     fx = futures_symbols[0]
60     eq = futures_symbols[1]
61     bond = futures_symbols[2]
62     
63     if self.data[fx].is_ready() and self.data[eq].is_ready() and self.data[bond].is_ready():
64         fx_perf = self.data[fx].performance()
65         eq_perf = self.data[eq].performance()
66         bond_perf = self.data[bond].performance()
67         
68         bond_w = 0
69         fx_w = 0
70         eq_w = 0
71         
72         # Bonds have a negative effect on FX and positive effect on Equities
73         bond_signum = np.sign(bond_perf)
74         fx_w -= bond_signum
75         eq_w += bond_signum
76         
77         # Equities have a negative effect on Bonds and negative effect on FX
78         eq_signum = np.sign(eq_perf)
79         bond_w -= eq_signum
80         fx_w -= eq_signum
81         
82         # FX has a positive effect on Equities and positive effect on Bonds
83         fx_signum = np.sign(fx_perf)
84         eq_w += fx_signum
85         bond_w += fx_signum
86         # inverse volatility sum of traded symbols
87         total_volatility = sum([ 1/self.data[x[0]].volatility() for x in [(fx,fx_w), (eq,eq_w), (bond, bond_w)] if x[1] != 0 ])
88         
89         # volatility weighting
90         if total_volatility != 0:
91             weight[fx] = ((1/self.data[fx].volatility()) / total_volatility) * np.sign(fx_w)
92             weight[eq] = ((1/self.data[eq].volatility()) / total_volatility) * np.sign(eq_w)
93             weight[bond] = ((1/self.data[bond].volatility()) / total_volatility) * np.sign(bond_w)
94         
95         traded_asset_classs_count += 1
96     
97 portfolio: List[PortfolioTarget] = []
98 if traded_asset_classs_count != 0:
99     weight_ratio = 1 / traded_asset_classs_count
100     portfolio = [PortfolioTarget(symbol, weight_ratio * w) for symbol, w in weight.items() if data.contains_key(symbol) and data[symbol]]
101 
102 self.SetHoldings(portfolio, True)
103def Rebalance(self):
104 self.rebalance_flag = True
105class SymbolData():
106def __init__(self, period):
107 self.price = RollingWindow[float](period)
108 self.period = period
109 
110def update(self, value) -> None:
111 self.price.Add(value)
112
113def performance(self) -> float:
114 result = self.price[0] / self.price[self.period-1] - 1
115 return result
116def volatility(self) -> float:
117 prices = np.array([x for x in self.price][:60])
118 result = prices[:-1] / prices[1:] - 1
119 result = np.std(result) * np.sqrt(252)
120 return result
121 
122def reset(self) -> None:
123 self.price.reset()
124def is_ready(self) -> bool:
125 return self.price.IsReady
126 
127# Quantpedia data.
128# NOTE: IMPORTANT: Data order must be ascending (datewise)
129class QuantpediaFutures(PythonData):
130_last_update_date:Dict[Symbol, datetime.date] = {}
131@staticmethod
132def get_last_update_date() -> Dict[Symbol, datetime.date]:
133return QuantpediaFutures._last_update_date
134def GetSource(self, config, date, isLiveMode):
135 return SubscriptionDataSource("data.quantpedia.com/backtesting_data/futures/{0}.csv".format(config.Symbol.Value), SubscriptionTransportMedium.RemoteFile, FileFormat.Csv)
136def Reader(self, config, line, date, isLiveMode):
137 data = QuantpediaFutures()
138 data.Symbol = config.Symbol
139 
140 if not line[0].isdigit(): return None
141 split = line.split(';')
142 
143 data.Time = datetime.strptime(split[0], "%d.%m.%Y") + timedelta(days=1)
144 data['back_adjusted'] = float(split[1])
145 data['spliced'] = float(split[2])
146 data.Value = float(split[1])
147 if config.Symbol.Value not in QuantpediaFutures._last_update_date:
148     QuantpediaFutures._last_update_date[config.Symbol.Value] = datetime(1,1,1).date()
149 if data.Time.date() > QuantpediaFutures._last_update_date[config.Symbol.Value]:
150     QuantpediaFutures._last_update_date[config.Symbol.Value] = data.Time.date()
151 return data
152# Custom fee model
153class CustomFeeModel(FeeModel):
154def GetOrderFee(self, parameters):
155 fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
156 return OrderFee(CashAmount(fee, "USD"))