few bullish $SPY setups as on 4th Nov 2014

few bullish $SPY setups as on 4th Nov 2014

$SPY 4 Nov 2014 Stock Chart

with $SPY closing below the previous day’s low of 201.31 , below few bullish setup’s on $SPY as on 4th Nov 2014 close

1) $SPY closes below prev day’s low after closing at 250 day’s high as on previous day , data since 2009 

that is

  • a) during previous trading day $SPY closes at 250 day high ,
  • b) as on current trading day $SPY pulls back and closes below , previous day’s low
  • c) below the previous trading odds for $SPY longs for the next 1/2/3/4/5 trading days , data since 2009
Exit # Wins % Wins Avg% Med% Avg Win % Avg Loss % Pay Off Max Loss % PF OAPF
t+1 29 18 62.1 0.24 0.28 0.64 -0.43 1.50 -1.12 2.00 1.68
t+2 29 19 65.5 0.54 0.61 1.03 -0.41 2.54 -1.45 3.71 3.28
t+3 29 21 72.4 0.55 0.63 1.03 -0.74 1.41 -1.70 3.31 2.92
t+4 29 20 69.0 0.64 1.00 1.41 -1.08 1.31 -3.89 2.60 2.31
t+5 29 22 75.9 0.73 0.73 1.33 -1.17 1.14 -3.41 3.34 2.94
1st +’ve exit in 5 days 29 27 93.1 0.42 0.37 0.50 -0.69 0.73 -1.16 8.04 6.95

ps: PF: Profit Factor, and OAPF is the outlier adjusted profit factor ( which is profit factor recalculated after removing the maximum winner )

1st +’ve exit in 5 day’s , assumes the trader is long at the current close and exits at the first higher close than the current close ( i.e 4th Nov 2014 close ) , other wise exit with a loss at the end of the fifth trading day ( i.e . 11th Nov 2014)

Below the historical instances of SPY , returns for “$SPY closes below prev day’s low after closing at 250 day’s high as on previous day ” trading strategy , since 2009

Date Close t+1% t+2% t+3% t+4% t+5%
04-Nov-14 201.07 ?? ?? ?? ?? ??
27-Aug-14 199.32 -0.06 0.23 0.18 0.13 -0.02
25-Jul-14 196.8 0.04 -0.39 -0.38 -2.34 -2.64
07-Jul-14 196.59 -0.64 -0.2 -0.59 -0.45 0.05
14-May-14 187.28 -0.88 -0.53 -0.17 -0.8 0.04
03-Mar-14 182.44 1.4 1.5 1.73 1.77 1.72
02-Jan-14 180.4 -0.02 -0.3 0.31 0.33 0.39
10-Dec-13 177.3 -1.12 -1.45 -1.46 -0.85 -1.16
07-Nov-13 171.59 1.35 1.37 1.16 1.98 2.48
30-Oct-13 172.93 -0.28 -0.05 0.31 -0.01 0.5
16-Jul-13 163.53 0.26 0.8 0.98 1.18 0.97
22-May-13 161.12 -0.29 -0.37 0.22 -0.43 -0.06
01-May-13 153.69 0.93 1.95 2.21 2.73 3.2
03-Apr-13 150.73 0.4 -0.05 0.63 0.98 2.22
20-Feb-13 146.3 -0.61 0.37 -1.54 -0.87 0.38
04-Feb-13 144.56 1.01 1.09 0.95 1.51 1.49
17-Sep-12 140.11 -0.08 -0.03 -0.02 -0.06 -0.21
10-Sep-12 137.03 0.28 0.61 2.15 2.6 2.25
22-Feb-12 128.65 0.44 0.66 0.83 1.13 0.73
10-Feb-12 127.07 0.75 0.62 0.15 1.26 1.53
22-Feb-11 122.12 -0.61 -0.69 0.38 1 -0.69
28-Jan-11 118.31 0.75 2.37 2.17 2.39 2.69
19-Jan-11 118.8 -0.13 0.09 0.66 0.72 1.11
30-Dec-10 116.46 0.02 1.06 1 1.53 1.32
15-Dec-10 114.36 0.58 0.68 0.93 1.57 1.88
16-Apr-10 108.88 0.38 1.28 1.09 1.4 2.06
15-Jan-10 103.24 1.25 0.22 -1.7 -3.89 -3.41
12-Jan-10 103.26 0.84 1.11 -0.02 1.23 0.2
27-Nov-09 99.01 0.33 1.58 1.54 0.74 1.31
12-Nov-09 98.52 0.55 2 2.12 2.06 0.73

27/29 times $SPY closed higher than the entry price over the next 5 trading days at some point of time , highlighted in the red were the two instances when $SPY failed to close above the entry price .

2) $SPY closes below previous day’s low on a Tuesday , data since Jan 2009

below the previous trading odds for $SPY longs for the next 1/2/3/4/5 trading days

Exit # Wins % Wins Avg% Med% Avg Win % Avg Loss % Pay Off Max Loss % PF OAPF
t+1 64 40 62.5 0.30 0.34 0.90 -0.70 1.28 -2.51 2.09 1.92
t+2 64 39 60.9 0.32 0.26 1.30 -1.22 1.06 -4.17 1.57 1.42
t+3 64 36 56.3 0.20 0.23 1.39 -1.33 1.05 -5.34 1.40 1.28
t+4 64 36 56.3 0.11 0.35 1.52 -1.72 0.89 -10.54 1.32 1.21
t+5 64 44 68.8 0.60 0.89 1.69 -1.80 0.94 -6.38 2.22 2.08
1st +’ve exit in 5 days 64 59 92.2 0.58 0.51 0.81 -2.11 0.38 -4.89 4.95 4.64

59/64 times $SPY closed higher over the next 5 trading days at some point time , with an average gain of 0.58% and a median gain of 0.51% 

conclusion : it is bullish to buy that pullback

you might want to consider subscribing to our daily quant rants email  !!

contact us for your quant trading programming needs in python and or R 

check out Quant-Ideas  to aid the short term trader to find high probability winning trades

Leave a Reply