Accidentally sent above post before I finished. I too have been working with the dual momentum algo. I made a couple of changes to better conform to Mr. Antonacci's strategy. I've included my changes below. I noticed a few things. One is the monthly rebalance should occur on the last trading day of the month instead of the first. I increased the return period to 254 days from 100. Finally I noticed that when going to bonds Mr. Antonacci's strategy is 100% in AGG so I commented out a line and changed the weighting to 1.0 for AGG.
"""
This is a template algorithm on Quantopian for you to adapt and fill in.
"""
from future import division
from quantopian.algorithm import attach_pipeline, pipeline_output
from quantopian.pipeline import Pipeline
from quantopian.pipeline.data.builtin import USEquityPricing
from quantopian.pipeline.factors import AverageDollarVolume, CustomFactor, Returns
from quantopian.pipeline import CustomFilter
import numpy as np
import pandas as pd
from scipy import stats
class SecurityInList(CustomFactor):
inputs = []
window_length = 1
securities = []
def compute(self, today, assets, out):
out[:] = np.in1d(assets, self.securities)
def initialize(context):
"""
Called once at the start of the algorithm.
"""
set_benchmark(sid(21513))
# Rebalance every month, 1 hour after market open.
schedule_function(my_assign_weights, date_rules.month_end(),time_rules.market_open())
schedule_function(my_rebalance, date_rules.month_end(), time_rules.market_open(hours=1))
# Record tracking variables at the end of each day.
schedule_function(my_record_vars, date_rules.every_day(), time_rules.market_close())
# Create our dynamic stock selector.
context.return_period = 254
"""
sid 21513 = IVV: iShares Core S&P 500 ETF
sid 33486 = VEU: Vanguard FTSE All-World ex-US ETF
sid 23911 = SHY: iShares 1-3 year Treasury Bond ETF
sid 25485 = AGG: iShares Core U. S. Aggregate Bond ETF
"""
context.mom1 = mom1 = sid(21513)
context.mom2 = mom2 = sid(33486)
context.tbill = tbill = sid(23911)
#context.tlt = tlt = sid(23921)
context.agg = agg = sid(25485)
sec_list = [mom1,mom2,tbill,agg]
attach_pipeline(make_pipeline(sec_list, context), 'my_pipeline')
set_commission(commission.PerShare(cost=0, min_trade_cost=0))
# Momentum ETFs
def make_pipeline(sec_list, context):
"""
A function to create our dynamic stock selector (pipeline). Documentation on
pipeline can be found here: https://www.quantopian.com/help#pipeline-title
"""
# Return Factors
mask = SecurityInList()
mask.securities = sec_list
mask = mask.eq(1)
yr_returns = Returns(window_length=context.return_period, mask=mask)
pipe = Pipeline(
screen = mask,
columns = {
'yr_returns': yr_returns
}
)
return pipe
def before_trading_start(context, data):
"""
Called every day before market open.
"""
context.output = pipeline_output('my_pipeline')
def my_assign_weights(context, data):
context.weights = pd.Series(index=context.output.index)
returns = context.output['yr_returns']
if returns[context.mom1] > returns[context.mom2]:
if returns[context.mom1] < 0:
#context.weights[context.tbill] = .5
context.weights[context.agg] = 1
elif returns[context.mom1] > returns[context.tbill]:
context.weights[context.mom1] = 1
else:
context.weights[context.agg] = 1
else:
if returns[context.mom2] > returns[context.tbill]:
context.weights[context.mom2] = 1
else:
context.weights[context.agg] = 1
context.weights.fillna(0,inplace=True)
def my_rebalance(context,data):
"""
Execute orders according to our schedule_function() timing.
"""
for stock,weight in context.weights.iteritems():
if data.can_trade(stock):
order_target_percent(stock,weight)
def my_record_vars(context, data):
"""
Plot variables at the end of each day.
"""
record(leverage=context.account.leverage)
def handle_data(context,data):
"""
Called every minute.
"""
pass
