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Monthly End-of-the-Month Strategy

To install Systematic Investor Toolbox (SIT) please visit About page.

The Monthly End-of-the-Month Strategy (MEOM) by Quanting Dutchman backtest and live signal.

The MEOM Strategy invests into the top 2 ETFs that are trading above a medium term moving avarage (WMA89) from the universe of 26 ETFs:

The best asset is selected using following ranking criteria:

Rank = MA( C/Ref(C,-2), 5 ) * Ref( MA( C/Ref(C,-2), 10 ), -5 )

Following report is based on Monthly re-balancing. Signal is generated one day prior to the month end, and execution is done at close at the month end.

The strategy enters positions in the top 2 ranked ETFs on the last day of the month at the close, and exits positions two days later at the close.

Load historical data from Yahoo Finance:

#*****************************************************************
# Load historical data
#*****************************************************************
library(SIT)
load.packages('quantmod')

tickers = 'DIA,EEM,EFA,EWH,EWJ,EWT,EWZ,FXI,GLD,GSG,IEF,ILF,IWM,IYR,QQQ,SPY,VNQ,XLB,XLE,XLF,XLI,XLP,XLU,XLV,XLY,XLK'

data <- new.env()

getSymbols.extra(tickers, src = 'yahoo', from = '1970-01-01', env = data, auto.assign = T, set.symbolnames = T, getSymbols.fn = getSymbols.fn, calendar=calendar)
  for(i in data$symbolnames) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)
bt.prep(data, align='keep.all', dates='1995::', fill.gaps=T)

print(last(data$prices))
  DIA EEM EFA EWH EWJ EWT EWZ FXI GLD GSG IEF ILF IWM IYR QQQ SPY VNQ XLB XLE XLF XLI XLP XLU XLV XLY XLK
2016-06-24 173.62 32.65 53.66 18.82 11.34 13.41 27.8 32.49 126 15.3 112.21 24.47 112.38 78.89 104.29 203.24 84.86 46.02 66.54 22.08 54.38 53.1 50.41 69.31 76.17 42.28 
#*****************************************************************
# Setup
#*****************************************************************
data$universe = data$prices > 0
prices = data$prices * data$universe
	n = ncol(prices)
  nperiods = nrow(prices)

Code Strategy Rules:

#*****************************************************************
# Code Strategy
#******************************************************************
# BuyRule = C > WMA(C, 89)
buy.rule = prices > bt.apply.matrix(prices, function(x) { WMA(x, 89) } )		
  buy.rule = ifna(buy.rule, F)

# 2-day returns
ret2 = ifna(prices / mlag(prices, 2), 0)

# Rank2 = MA( C/Ref(C,-2), 5 ) * Ref( MA( C/Ref(C,-2), 10 ), -5 )
position.score = bt.apply.matrix(ret2, SMA, 5) * mlag( bt.apply.matrix(ret2, SMA, 10), 5)
	position.score[!buy.rule] = NA

period.ends2 = iif(period.ends + 2 > nperiods, nperiods, period.ends + 2)

weight = ntop(position.score[period.ends,], 2)

models = list()

data$weight[] = NA
	data$weight[period.ends,] = 	weight
	data$weight[period.ends2,] = 0		
models$strategy = bt.run.share(data, clean.signal=F, trade.summary=T, silent=T, do.lag = 1 + out$delayLen)

plot of chunk plot-7

#Strategy Performance:

  strategy
Period Jan1995 - Jun2016
Cagr 6.54
Sharpe 0.98
DVR 0.94
R2 0.96
Volatility 6.69
MaxDD -10.26
Exposure 8.99
Win.Percent 62.88
Avg.Trade 0.31
Profit.Factor 2.05
Num.Trades 458

plot of chunk plot-7

#Monthly Results for strategy :

  Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Year MaxDD
1995   0.0 0.0 0.0 0.0 -0.2 0.7 -0.4 1.4 -0.4 1.4 1.4 4.0 -0.5
1996 1.3 0.0 2.2 1.4 -1.5 1.0 0.5 0.0 0.7 1.5 0.2 -1.2 6.1 -1.7
1997 1.0 0.9 0.1 0.0 1.6 1.7 0.4 -0.1 0.0 1.9 0.0 2.0 10.0 -1.3
1998 0.7 2.1 1.0 -1.0 0.9 0.3 1.0 -4.2 0.0 0.0 2.1 0.5 3.3 -7.7
1999 1.4 -0.3 -0.1 2.9 1.8 0.3 1.7 0.2 0.3 2.5 -0.9 4.4 14.9 -1.0
2000 -3.3 2.4 -1.5 1.2 -1.2 2.2 -0.6 1.9 -2.2 -0.4 -1.2 -0.2 -3.1 -5.9
2001 -2.1 -3.0 0.8 0.0 -1.8 0.1 1.0 0.7 1.5 0.0 2.2 3.1 2.4 -6.1
2002 1.6 -2.0 4.2 -1.1 1.3 -0.1 -1.7 0.0 -1.0 0.0 2.7 -2.6 1.2 -4.5
2003 3.9 0.3 0.1 5.5 0.9 1.2 4.6 -1.0 3.1 2.9 1.0 2.1 27.3 -1.0
2004 4.9 -0.2 0.7 2.2 1.3 -0.2 -2.4 0.5 2.1 3.2 -0.2 -2.4 9.6 -3.2
2005 -5.6 2.1 -0.9 -0.4 0.2 1.5 -0.7 3.0 1.6 -1.5 1.0 1.9 1.9 -5.8
2006 3.7 -2.9 2.8 1.1 2.0 -0.4 0.2 0.2 1.2 0.1 1.1 1.1 10.5 -2.9
2007 -0.4 0.6 -0.8 0.7 1.1 2.6 3.2 -0.6 0.0 4.8 -4.4 -0.3 6.4 -5.0
2008 2.5 -1.7 0.6 5.3 1.9 -3.4 1.9 -0.9 1.4 -1.0 0.0 -1.1 5.3 -4.1
2009 -2.2 0.1 -2.8 6.6 4.8 3.4 -2.0 7.1 -4.4 -2.8 1.9 2.3 11.7 -7.7
2010 1.6 1.2 1.0 2.1 0.8 0.2 -1.4 2.4 -0.5 1.5 1.1 3.9 14.7 -4.0
2011 0.7 1.0 0.2 0.7 0.0 0.1 2.1 1.7 -2.0 -1.2 1.4 0.0 4.7 -5.3
2012 -1.6 3.1 -0.1 0.6 0.8 0.1 0.6 -1.0 0.2 1.0 1.9 0.2 5.7 -1.6
2013 2.0 -0.2 1.3 1.4 -0.5 -0.4 0.5 1.4 0.2 0.6 0.5 -1.6 5.1 -2.0
2014 -1.4 -0.6 1.1 2.5 -0.8 0.7 -0.6 0.9 1.0 -0.4 -0.4 -0.6 1.4 -2.6
2015 -1.3 1.9 0.3 2.0 0.3 -0.1 0.6 -0.2 0.2 0.6 1.5 -0.1 5.6 -1.3
2016 0.7 1.2 1.0 -1.6 -5.7 0.4             -4.0 -8.1
Avg 0.4 0.3 0.5 1.5 0.4 0.5 0.5 0.6 0.2 0.6 0.6 0.6 6.6 -3.8

plot of chunk plot-7 plot of chunk plot-7

#Trades for strategy :

strategy weight entry.date exit.date nhold entry.price exit.price return
QQQ 50 2015-07-31 2015-08-04 4 110.71 110.16 -0.25
XLY 50 2015-07-31 2015-08-04 4 78.98 79.05 0.04
IEF 100 2015-08-31 2015-09-02 2 104.77 104.95 0.18
IEF 100 2015-09-30 2015-10-02 2 106.42 107.02 0.56
QQQ 50 2015-10-30 2015-11-03 4 112.35 114.02 0.74
XLK 50 2015-10-30 2015-11-03 4 43 43.61 0.71
IWM 50 2015-11-30 2015-12-02 2 118.23 117.73 -0.21
XLY 50 2015-11-30 2015-12-02 2 79.83 79.95 0.07
IYR 50 2015-12-31 2016-01-05 5 73.53 74.01 0.33
VNQ 50 2015-12-31 2016-01-05 5 78.14 78.69 0.35
GLD 50 2016-01-29 2016-02-02 4 106.95 108.09 0.53
XLU 50 2016-01-29 2016-02-02 4 44.72 45.34 0.69
GLD 50 2016-02-29 2016-03-02 2 118.64 118.68 0.02
XLI 50 2016-02-29 2016-03-02 2 51.57 52.62 1.02
EWZ 50 2016-03-31 2016-04-04 4 26.3 25.55 -1.43
VNQ 50 2016-03-31 2016-04-04 4 83.05 82.83 -0.14
EWZ 50 2016-04-29 2016-05-03 4 29.51 27.7 -3.07
ILF 50 2016-04-29 2016-05-03 4 26.96 25.54 -2.64
EWT 50 2016-05-31 2016-06-02 2 13.34 13.49 0.56
XLK 50 2016-05-31 2016-06-02 2 43.98 43.83 -0.17

#Signals for strategy :

  DIA EEM EFA EWH EWJ EWT EWZ FXI GLD GSG IEF ILF IWM IYR QQQ SPY VNQ XLB XLE XLF XLI XLP XLU XLV XLY XLK
2014-10-30 0 0 0 0 0 0 0 0 0 0 0 0 50 0 0 0 0 0 0 0 50 0 0 0 0 0
2014-11-26 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 50 0 0 0 0 0 0 50 0
2014-12-30 0 0 0 0 0 0 0 0 0 0 0 0 50 0 0 0 0 0 0 0 0 0 50 0 0 0
2015-01-29 0 0 0 50 0 50 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2015-02-26 0 0 0 0 50 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 50 0
2015-03-30 0 0 0 50 0 0 0 50 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2015-04-29 0 0 0 0 0 50 50 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2015-05-28 0 0 0 0 0 0 0 0 0 0 0 0 0 0 50 0 0 0 0 0 0 0 0 50 0 0
2015-06-29 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 0 0
2015-07-30 0 0 0 0 0 0 0 0 0 0 0 0 0 0 50 0 0 0 0 0 0 0 0 0 50 0
2015-08-28 0 0 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2015-09-29 0 0 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2015-10-29 0 0 0 0 0 0 0 0 0 0 0 0 0 0 50 0 0 0 0 0 0 0 0 0 0 50
2015-11-27 0 0 0 0 0 0 0 0 0 0 0 0 50 0 0 0 0 0 0 0 0 0 0 0 50 0
2015-12-30 0 0 0 0 0 0 0 0 0 0 0 0 0 50 0 0 50 0 0 0 0 0 0 0 0 0
2016-01-28 0 0 0 0 0 0 0 0 50 0 0 0 0 0 0 0 0 0 0 0 0 0 50 0 0 0
2016-02-26 0 0 0 0 0 0 0 0 50 0 0 0 0 0 0 0 0 0 0 0 50 0 0 0 0 0
2016-03-30 0 0 0 0 0 0 50 0 0 0 0 0 0 0 0 0 50 0 0 0 0 0 0 0 0 0
2016-04-28 0 0 0 0 0 0 50 0 0 0 0 50 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2016-05-27 0 0 0 0 0 50 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 50

For your convenience, the Strategy-MEOM report can also be downloaded and viewed the pdf format.

For more details please check the MEOM strategy code at bt.meom.test() function in bt.test.r at github

(this report was produced on: 2016-06-25)