I’m trying to rewrite the code to follow Wouter Keller’s Bold Asset Allocation - Aggressive strategy, which means holding only one position at a time.
I have tried to modify the code myself using ChatGPT, but it did not help. Can someone help me identify where I need to make adjustments to ensure that the strategy only holds one position at a time?
Here is the code,
#region imports
from AlgorithmImports import *
#endregion
#See: Relative and Absolute Momentum in Times of Rising/Low Yields: Bold Asset Allocation (BAA) by Wouter J. Keller :: SSRN
import pandas as pd
import numpy as np
class BoldAssetAllocation(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2015,1,1) # Set Start Date
self.start_cash = 100000
self.SetCash(self.start_cash) # Set Strategy Cash
self.SetBenchmark('SPY')
# Algo Parameters
self.prds = [1,3,6,12]
self.prdwts = np.array([12,6,2,1])
self.LO, self.LD, self.LP, self.B, self.TO, self.TD = [12,12,0,1,1,3]
self.hprd = max(self.prds+[self.LO,self.LD])*21+50
# Assets
self.canary = ['SPY','EFA','EEM','BND']
self.offensive = ['QQQ','EFA','EEM','BND']
self.defensive = ['BIL','BND','DBC','IEF','LQD','TIP','TLT']
self.safe = 'BIL'
# repeat safe asset so it can be selected multiple times
self.alldefensive = self.defensive + [self.safe] * max(0,self.TD - sum([1*(e==self.safe) for e in self.defensive]))
self.eqs = list(dict.fromkeys(self.canary+self.offensive+self.alldefensive))
for eq in self.eqs:
self.AddEquity(eq,Resolution.Minute)
# monthly rebalance
self.Schedule.On(self.DateRules.MonthStart(self.canary[0]),self.TimeRules.AfterMarketOpen(self.canary[0],30),self.rebal)
self.Trade = True
def rebal(self):
self.Trade = True
def OnData(self, data):
if self.Trade:
# Get price data and trading weights
h = self.History(self.eqs,self.hprd,Resolution.Daily)['close'].unstack(level=0)
wts = self.trade_wts(h)
# trade
port_tgt = [PortfolioTarget(x,y) for x,y in zip(wts.index,wts.values)]
self.SetHoldings(port_tgt)
self.Trade = False
def trade_wts(self,hist):
# initialize wts Series
wts = pd.Series(0,index=hist.columns)
# end of month values
h_eom = (hist.loc[hist.groupby(hist.index.to_period('M')).apply(lambda x: x.index.max())]
.iloc[:-1,:])
# =====================================
# check if canary universe is triggered
# =====================================
# build dataframe of momentum values
mom = h_eom.iloc[-1,:].div(h_eom.iloc[[-p-1 for p in self.prds],:],axis=0)-1
mom = mom.loc[:,self.canary].T
# Determine number of canary securities with negative weighted momentum
n_canary = np.sum(np.sum(mom.values*self.prdwts,axis=1)<0)
# % equity offensive
pct_in = 1-min(1,n_canary/self.B)
# =====================================
# get weights for offensive and defensive universes
# =====================================
# determine weights of offensive universe
if pct_in > 0:
# price / SMA
mom_in = h_eom.iloc[-1,:].div(h_eom.iloc[[-t for t in range(1,self.LO+1)]].mean(axis=0),axis=0)
mom_in = mom_in.loc[self.offensive].sort_values(ascending=False)
# equal weightings to top relative momentum securities
in_wts = pd.Series(pct_in/self.TO,index=mom_in.index[:self.TO])
wts = pd.concat([wts,in_wts])
# determine weights of defensive universe
if pct_in < 1:
# price / SMA
mom_out = h_eom.iloc[-1,:].div(h_eom.iloc[[-t for t in range(1,self.LD+1)]].mean(axis=0),axis=0)
mom_out = mom_out.loc[self.alldefensive].sort_values(ascending=False)
# equal weightings to top relative momentum securities
out_wts = pd.Series((1-pct_in)/self.TD,index=mom_out.index[:self.TD])
wts = pd.concat([wts,out_wts])
wts = wts.groupby(wts.index).sum()
return wts
and here is the website it was sourced from.
I was planning to run it in (great site): Algorithmic Trading Platform - QuantConnect.com