Keller’s & Keunig’s Protective Asset Allocation

1from AlgorithmImports import *
2#endregion
3
4class KellersKeunigsPAA(QCAlgorithm):
5
6def Initialize(self):
7self.SetStartDate(2000, 1, 1)  
8self.SetCash(100000)
9
10# Parameters for algorithm 
11self.lookback:int = 12 * 21                 # Lookback period (in months)
12self.protection:int = 2                     # Protection factor = 0 (low), 1, 2 (high)
13self.topM:int = 6                           # topM is the max number of equities
14self.n_levels:int = 2                       # number of discrete levels for bond_fraction (>=2)
15self.cash_universe:List[str] = ["SHY"]      # risk free asset to move into for protection 
16self.N_safe:int = int(len(self.cash_universe))
17
18self.risky_universe:List[str] = [
19    "SPY", "QQQ", "IWM",
20    "VGK", "EWJ", "EEM",
21    "IYR", "GSG", "GLD"
22    ]
23
24self.N_eq:int = len(self.risky_universe)
25
26sec = self.AddSecurity(SecurityType.Equity, "SHY", Resolution.Minute)
27sec.MarketPrice = self.GetLastKnownPrice(sec)
28
29self.symbol_objs = []
30
31for ticker in list(self.risky_universe):
32    self.symbol_objs.append(self.AddSecurity(SecurityType.Equity, ticker, Resolution.Minute).Symbol)
33
34for symbol_obj in self.symbol_objs:
35    symbol_obj.lookback_ma = self.SMA(symbol_obj, self.lookback, Resolution.Daily)
36    
37self.SetWarmup(self.lookback, Resolution.Daily)
38self.recent_month:int = -1
39
40def OnData(self, data:Slice) -> None:
41if self.IsWarmingUp: return
42
43if not (self.cash_universe[0] in data and data[self.cash_universe[0]]):
44    return
45
46if not(self.Time.hour == 9 and self.Time.minute == 45):
47    return
48
49if self.Time.month == self.recent_month:
50    return
51self.recent_month = self.Time.month
52
53# poll the Risk Universe set to determine the number of assets with positive momentum
54n = 0
55for symbol_obj in self.symbol_objs:
56    if symbol_obj in data and data[symbol_obj]:
57        if symbol_obj.lookback_ma.IsReady:
58            price = data[symbol_obj].Value
59            sma = symbol_obj.lookback_ma.Current.Value
60            if price > sma: n += 1
61
62# Calculate the bond fraction based on N_eq, prot, and n
63# This is the portion to be invested in safe harbor
64# Calculate equity fraction and weight per equity (frac_eq, w_eq) 
65# Limit bond_fraction to a discrete number of levels (n_levels >=2)
66
67# n1 = a*N/4
68n1:float = (self.protection * self.N_eq) / 4.0
69# BF = (N-n)/(N-n1)
70bond_fraction:float = min(1.0, (float(self.N_eq) - float(n)) / (float(self.N_eq) - n1))
71w_safe:float = bond_fraction
72
73# calculate the MOM for each equity determine the number of equities to be purchases
74N = 0
75for symbol_obj in self.symbol_objs:
76    symbol_obj.MOM = 0.
77
78    if symbol_obj in data and data[symbol_obj]:
79        if symbol_obj.lookback_ma.IsReady:
80            price = data[symbol_obj].Value
81            sma = symbol_obj.lookback_ma.Current.Value
82            symbol_obj.MOM = (price / sma) - 1
83            if symbol_obj.MOM > 0.0: N+=1
84
85if N == 0:
86    self.Liquidate()
87    return
88
89frac_eq:float = 1.0 - w_safe
90n_eq:int = min(N, self.topM)
91w_eq:float = 0.
92if N > 0: w_eq = frac_eq / float(n_eq)
93mom_threshold = sorted([i.MOM for i in self.symbol_objs if i.MOM != 0.], reverse=True)[n_eq - 1]
94
95if frac_eq > 0.0:
96    for symbol_obj in self.symbol_objs:
97        if symbol_obj.MOM >= float(mom_threshold):
98            self.SetHoldings(symbol_obj, w_eq)
99        else:
100            if self.Portfolio[symbol_obj].Invested:
101                self.Liquidate(symbol_obj)
102
103    self.SetHoldings(self.cash_universe[0], w_safe)
104else:
105    for symbol_obj in self.symbol_objs:
106        if self.Portfolio[symbol_obj].Invested:
107            self.Liquidate(symbol_obj)
108
109    self.SetHoldings(self.cash_universe[0], 1.0)