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Turn of the Month Seasonality

23 Mar 2015

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The QuantDare wrote a detailed post about Turn of the Month Seasonality for S&P 500

Below I will try to adapt a code from the posts:

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

tickers = 'SPY+^GSPC'

data = new.env()
getSymbols.extra(tickers, src = 'yahoo', from = '1980-01-01', env = data, auto.assign = T)

for(i in ls(data))
	data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)

bt.prep(data, align='remove.na')

#*****************************************************************
# Setup
#*****************************************************************
prices = data$prices

models = list()

universe = prices > 0

key.date.index = date.month.ends(data$dates, F)
key.date = NA * prices
	key.date[key.date.index,] = T	

#*****************************************************************
# Turn of the Month Seasonality
#*****************************************************************
signals = list()
for(i in 10:1) signals[[paste0('P',i)]] = -i	
signals$T0 = 0
for(i in 1:10) signals[[paste0('N',i)]] = i	
	signals = calendar.signal(key.date, signals)

models = calendar.strategy(data, signals, universe = universe)

#*****************************************************************
# Create Report
#*****************************************************************
print(plotbt.strategy.sidebyside(models, make.plot=F, return.table=T, perfromance.fn=engineering.returns.kpi))
  P10 P9 P8 P7 P6 P5 P4 P3 P2 P1 T0 N1 N2 N3 N4 N5 N6 N7 N8 N9 N10
Period Jan1980 - Mar2015 Jan1980 - Mar2015 Jan1980 - Mar2015 Jan1980 - Mar2015 Jan1980 - Mar2015 Jan1980 - Mar2015 Jan1980 - Mar2015 Jan1980 - Mar2015 Jan1980 - Mar2015 Jan1980 - Mar2015 Jan1980 - Mar2015 Jan1980 - Mar2015 Jan1980 - Mar2015 Jan1980 - Mar2015 Jan1980 - Mar2015 Jan1980 - Mar2015 Jan1980 - Mar2015 Jan1980 - Mar2015 Jan1980 - Mar2015 Jan1980 - Mar2015 Jan1980 - Mar2015
Cagr 1.9 -0.65 0.31 0.34 -0.93 -0.65 -0.07 1.44 0.8 0.85 0.58 2.48 1.31 0.63 -0.12 -0.52 -0.32 -0.3 -0.08 1.56 0.3
Sharpe 0.52 -0.1 0.1 0.11 -0.21 -0.15 0 0.37 0.24 0.23 0.18 0.59 0.37 0.19 -0.01 -0.12 -0.07 -0.06 -0.01 0.36 0.1
DVR 0.47 -0.05 0 0 -0.19 -0.05 0 0.27 0.18 0.21 0.04 0.55 0.29 0.09 0 -0.04 -0.03 -0.02 0 0.29 0.05
R2 0.9 0.56 0 0 0.88 0.35 0.13 0.71 0.74 0.91 0.2 0.93 0.79 0.49 0 0.32 0.51 0.32 0.19 0.8 0.55
Volatility 3.76 5.17 3.89 3.88 3.98 3.78 3.86 4.03 3.49 3.94 3.61 4.28 3.69 3.57 4.04 3.63 3.77 3.92 3.47 4.53 3.72
MaxDD -9.08 -35.81 -19.38 -23.67 -38.64 -27.31 -14.71 -12.58 -14.08 -9.99 -18.44 -11.97 -14.35 -19.98 -23.9 -24.09 -27.5 -33.36 -17.31 -10.09 -17.15
Exposure 4.75 4.75 4.75 4.75 4.75 4.75 4.75 4.75 4.75 4.75 4.75 4.75 4.75 4.75 4.75 4.75 4.75 4.75 4.75 4.75 4.75
Win.Percent 59.24 53.32 51.18 48.58 50.24 48.1 52.61 53.55 55.69 54.5 52.37 59 55.92 52.84 50.24 48.82 51.9 50.95 53.55 57.82 54.74
Avg.Trade 0.16 -0.04 0.03 0.03 -0.07 -0.05 0 0.13 0.07 0.08 0.05 0.21 0.11 0.06 0 -0.04 -0.02 -0.02 0 0.14 0.03
Profit.Factor 1.52 0.87 1.07 1.08 0.84 0.87 1 1.4 1.2 1.18 1.12 1.64 1.34 1.13 0.98 0.89 0.91 0.95 0.98 1.38 1.07
Num.Trades 422 422 422 422 422 422 422 422 422 422 422 422 422 422 422 422 422 422 422 422 422 
# custom stats	
out = sapply(models, function(x) list(
	CAGR = 100*compute.cagr(x$equity),
	MD = 100*compute.max.drawdown(x$equity),
	Win = x$trade.summary$stats['win.prob', 'All'],
	Profit = x$trade.summary$stats['profitfactor', 'All']
))	
performance.barchart.helper(out, sort.performance = F)

plot of chunk plot-2

A few days before and a few days after the month end show great performance with low draw downs.

(this report was produced on: 2015-03-24)

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