Size Factor vs. Monetary Policy Regime

1from AlgorithmImports import *
2# endregion
3
4class SizeFactorvsMonetaryPolicyRegime(QCAlgorithm):
5
6def Initialize(self) -> None:
7 self.SetStartDate(1999, 1, 1)
8 self.SetCash(100000)
9
10 self.large_cap:Symbol = self.AddEquity("SPY", Resolution.Daily).Symbol
11 self.small_cap:Symbol = self.AddEquity("IWM", Resolution.Daily).Symbol
12 self.leverage:int = 5
13
14 for symbol in [self.large_cap] + [self.small_cap]:
15     self.Securities[symbol.Value].SetLeverage(self.leverage)
16 
17 # changes in FED policy
18 dates_str_restrictive:List[str] = ["24.08.1999", "30.06.2004", "14.12.2016"]
19 dates_str_expansive:List[str] = ["03.01.2001", "18.09.2007", "31.7.2019"]
20 self.dates_res:List[datetime.date] = [datetime.strptime(x, "%d.%m.%Y").date() for x in dates_str_restrictive]   # datetime type
21 self.dates_exp:List[datetime.date] = [datetime.strptime(x, "%d.%m.%Y").date() for x in dates_str_expansive]     # datetime type
22
23 self.easing_flag:bool = None
24 self.selection_flag:bool = False
25 self.last_target_rate:float = None
26 self.target_rate:Symbol = self.AddData(FederalTargetRange, 'DFEDTARU', Resolution.Daily).Symbol
27 self.Schedule.On(self.DateRules.MonthStart(self.large_cap), self.TimeRules.AfterMarketOpen(self.large_cap), self.Selection)
28
29def OnData(self, data: Slice) -> None:
30 curr_date:datetime.date = self.Time.date()
31 if curr_date in self.dates_exp:
32     self.easing_flag = True
33 if curr_date in self.dates_res:
34     self.easing_flag = False
35
36 # monthly rebalance
37 if self.selection_flag:
38     self.selection_flag = False
39
40     # check target rate data arrival
41     ftr_last_update_date:Dict[Symbol, datetime.date] = FederalTargetRange.get_last_update_date()
42     if self.Securities[self.target_rate].GetLastData():
43         if self.target_rate in ftr_last_update_date and self.Time.date() >= ftr_last_update_date[self.target_rate]: 
44             self.Liquidate()
45             return
46
47         curr_target_rate = self.Securities[self.target_rate].Price
48
49         # switch to external data source
50         if curr_date > self.dates_exp[-1]:
51             if self.last_target_rate:
52                 if curr_target_rate > self.last_target_rate:
53                     self.easing_flag = False
54                 elif curr_target_rate < self.last_target_rate:
55                     self.easing_flag = True
56
57         self.last_target_rate = curr_target_rate
58 
59     if self.easing_flag is not None: 
60         if self.easing_flag and not self.Portfolio.Invested:
61             self.SetHoldings(self.small_cap, 1)
62             self.SetHoldings(self.large_cap, -1)
63         elif not self.easing_flag:
64             self.Liquidate()
65
66def Selection(self) -> None:
67 self.selection_flag = True
68
69# source: https://fred.stlouisfed.org/series/DFEDTARU
70class FederalTargetRange(PythonData):
71def GetSource(self, config, date, isLiveMode):
72 return SubscriptionDataSource('data.quantpedia.com/backtesting_data/economic/DFEDTARU.csv', SubscriptionTransportMedium.RemoteFile, FileFormat.Csv)
73
74_last_update_date:Dict[Symbol, datetime.date] = {}
75
76@staticmethod
77def get_last_update_date() -> Dict[Symbol, datetime.date]:
78return FederalTargetRange._last_update_date
79
80def Reader(self, config, line, date, isLiveMode):
81 data = FederalTargetRange()
82 data.Symbol = config.Symbol
83
84 if not line[0].isdigit(): return None
85 split = line.split(';')
86 
87 # Parse the CSV file's columns into the custom data class
88 data.Time = datetime.strptime(split[0], "%Y-%m-%d") + timedelta(days=1)
89 data.Value = float(split[1])
90
91 if config.Symbol not in FederalTargetRange._last_update_date:
92     FederalTargetRange._last_update_date[config.Symbol] = datetime(1,1,1).date()
93 if data.Time.date() > FederalTargetRange._last_update_date[config.Symbol]:
94     FederalTargetRange._last_update_date[config.Symbol] = data.Time.date()
95 
96 return data