Lethargic Asset Allocation

1from AlgorithmImports import *
2from typing import List
3from dateutil.relativedelta import relativedelta, FR
4# endregion
5
6class LethargicAssetsAllocation(QCAlgorithm):
7
8def Initialize(self) -> None:
9 self.SetStartDate(2010, 1, 1)
10 self.SetCash(100000)
11 
12 # set assets variables
13 self.static_universe:List[str] = ['IWD', 'GLD', 'IEF']
14 self.risky_asset:str = 'QQQ'
15 self.safe_asset:str = 'SHY'
16 
17 self.spy_period:int = 10 * 21
18 self.ue_period:int = 12
19
20 # warm up of indicators
21 self.SetWarmup(self.ue_period * 31, Resolution.Daily)
22
23 for ticker in self.static_universe + [self.risky_asset, self.safe_asset]:
24     self.AddEquity(ticker, Resolution.Daily)
25
26 # SMA assets
27 self.spy:Symbol = self.AddEquity('SPY', Resolution.Daily).Symbol
28 self.ue:Symbol = self.AddData(UnemploymentRate, 'UE', Resolution.Daily).Symbol
29
30 self.spy_sma = self.SMA(self.spy, self.spy_period, Resolution.Daily)
31 self.ue_sma = self.SMA(self.ue, self.ue_period, Resolution.Daily)
32
33def OnData(self, data: Slice) -> None:
34 if self.IsWarmingUp:
35     return
36
37 # rebalance when 'UE' data are in - monthly
38 if data.ContainsKey(self.ue) and data.ContainsKey(self.spy) and data[self.spy] and data[self.ue]:
39     # both SMA indicators are warmed up and ready
40     if all(sma.IsReady for sma in [self.spy_sma, self.ue_sma]):
41         invested_tickers:List[str] = [x.Key.Value for x in self.Portfolio if x.Value.Invested]
42         traded_universe:List[str] = self.static_universe if not self.Portfolio.Invested else invested_tickers
43
44         # change allocation of assets
45         if not self.Portfolio.Invested:
46             # both trends are negative - UE rate has risen
47             if data[self.spy].Value < self.spy_sma.Current.Value and data[self.ue].Value > self.ue_sma.Current.Value:
48                 traded_universe += [self.safe_asset]
49             else:
50                 traded_universe += [self.risky_asset]
51         else:
52             # SPY trend is positive
53             if data[self.spy].Value > self.spy_sma.Current.Value:
54                 if self.risky_asset not in traded_universe:
55                     traded_universe = list(map(lambda x: x.replace(self.safe_asset, self.risky_asset), traded_universe))
56         
57         # firstly, liquidate symbols that should not be held
58         for ticker in invested_tickers:
59             if ticker not in traded_universe:
60                 self.Liquidate(ticker)
61         
62         # rebalance new portfolio
63         for ticker in traded_universe:
64             if ticker in data and data[ticker]:
65                 self.SetHoldings(ticker, 1 / len(traded_universe))
66 else:
67     if self.Portfolio.Invested:
68         last_update_date:datetime.date = UnemploymentRate.get_last_update_date()
69         
70         # custom data stopped comming in
71         if self.Securities[self.ue].GetLastData() and self.Time.date() > last_update_date:
72             self.Liquidate()
73
74class UnemploymentRate(PythonData):
75def GetSource(self, config: SubscriptionDataConfig, date: datetime, isLiveMode: bool) -> SubscriptionDataSource:
76 return SubscriptionDataSource('data.quantpedia.com/backtesting_data/economic/UNEMPLOYMENT_RATE.csv', SubscriptionTransportMedium.RemoteFile, FileFormat.Csv)
77
78_last_update_date:datetime.date = datetime(1,1,1).date()
79
80@staticmethod
81def get_last_update_date() -> datetime.date:
82return UnemploymentRate._last_update_date
83
84def Reader(self, config: SubscriptionDataConfig, line: str, date: datetime, isLiveMode: bool) -> BaseData:
85 data = UnemploymentRate()
86 data.Symbol = config.Symbol
87
88 if not line[0].isdigit(): return None
89 split = line.split(';')
90 
91 # Parse the CSV file's columns into the custom data class - first Friday of the month
92 data.Time = (datetime.strptime(split[0], '%Y-%m-%d').date() + relativedelta(weekday=FR(1))) + timedelta(days=1)
93
94 if data.Time.date() > UnemploymentRate._last_update_date:
95     UnemploymentRate._last_update_date = data.Time.date()
96
97 data.Value = float(split[1])
98 
99 return data