Tax Day Rally Trading odds for $SPY $QQQ $IWM $XLU $XLF $XLE $XLI $IYR $XLP $XLK

Tax Day Rally Trading odds 

Tax Day Cartoon

 

below the trading odds for various ETF’s with high volume , from the close of the Tax day ( i’e 15th Apr 2015 close , in this year’s close) till next 1/2/3/4/5 trading days , since the respective ETF’s inception

1) $SPY trading odds from Tax day’s close  (SPDR S&P 500 ETF)

Exit # Wins % Wins Avg% Med% Avg Win % Avg Loss % Pay Off Max Loss % PF OAPF STDEV T-Test
t+1 22 14 63.6 0.51 0.44 1.28 -0.83 1.54 -1.71 3.39 2.82 1.26 1.91
t+2 22 14 63.6 0.72 0.44 1.58 -0.77 2.04 -2.38 3.97 3.10 1.63 2.09
t+3 22 11 50.0 0.68 -0.01 2.11 -0.75 2.83 -2.13 3.54 2.65 1.95 1.64
t+4 22 17 77.3 1.10 0.69 1.63 -0.73 2.25 -1.64 8.25 6.70 1.61 3.20
t+5 22 18 81.8 1.42 1.23 1.99 -1.14 1.75 -2.21 10.81 9.05 1.72 3.87
t+10 22 17 77.3 1.61 1.58 2.64 -1.88 1.41 -3.36 6.18 5.18 2.48 3.05
t+20 22 16 72.7 2.10 2.50 3.87 -2.62 1.48 -4.37 3.67 3.14 3.66 2.69
1st +’ve exit in 5 days 22 19 86.4 0.85 0.91 1.18 -1.24 0.96 -2.21 8.57 7.36 1.16 3.45

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 days assumes , one goes long at current close ( 15th Apr 2015 close) and exits at higher close than the entry in the next 5 trading days , otherwise exit with a loss at the end of the 5th trading day .. ( 22nd Apr 2015)

2) $QQQ trading odds from Tax day’s close ( PowerShares QQQ Trust, Ser 1 )

Exit # Wins % Wins Avg% Med% Avg Win % Avg Loss % Pay Off Max Loss % PF OAPF STDEV T-Test
t+1 16 10 62.5 0.93 0.97 1.99 -0.85 2.36 -2.91 3.93 3.03 1.75 2.12
t+2 16 10 62.5 1.33 1.21 3.26 -1.88 1.74 -7.47 2.10 1.29 3.98 1.34
t+3 16 8 50.0 1.50 0.22 4.78 -1.79 2.67 -5.36 1.91 1.01 5.45 1.10
t+4 16 10 62.5 2.19 0.86 4.50 -1.66 2.71 -5.44 2.54 1.49 5.42 1.62
t+5 16 14 87.5 2.19 1.20 2.77 -1.85 1.50 -3.08 7.50 5.42 3.27 2.68
t+10 16 12 75.0 2.15 1.20 3.71 -2.53 1.47 -7.90 5.85 4.19 4.64 1.85
t+20 16 11 68.8 1.87 2.00 5.03 -5.07 0.99 -8.97 2.28 1.84 5.73 1.31
1st +’ve exit in 5 days 16 15 93.8 1.43 1.64 1.73 -3.08 0.56 -3.08 5.88 4.78 1.57 3.64

 

3) $IWM trading odds from Tax day’s close (iShares Russell 2000)

Exit # Wins % Wins Avg% Med% Avg Win % Avg Loss % Pay Off Max Loss % PF OAPF STDEV T-Test
t+1 14 10 71.4 0.97 0.92 1.73 -0.93 1.85 -1.34 4.52 3.63 1.50 2.42
t+2 14 10 71.4 1.44 1.61 2.43 -1.06 2.30 -1.83 4.42 3.65 1.90 2.82
t+3 14 9 64.3 1.18 0.92 2.29 -0.82 2.80 -1.52 5.04 4.04 2.02 2.19
t+4 14 12 85.7 1.80 2.22 2.28 -1.07 2.13 -2.13 9.82 7.75 1.73 3.89
t+5 14 11 78.6 1.61 1.92 2.19 -0.55 3.99 -1.37 12.11 9.72 1.38 4.35
t+10 14 10 71.4 2.04 1.41 3.45 -1.49 2.32 -2.11 5.93 4.86 3.08 2.48
t+20 14 6 42.9 1.20 -0.75 5.99 -2.39 2.51 -5.91 1.50 0.97 4.86 0.93
1st +’ve exit in 5 days 14 13 92.9 1.19 0.92 1.39 -1.37 1.01 -1.37 9.93 8.04 1.28 3.47

 

4) $XLU  trading odds from Tax day’s close (Utilities Select Sector SPDR ETF)

Exit # Wins % Wins Avg% Med% Avg Win % Avg Loss % Pay Off Max Loss % PF OAPF STDEV T-Test
t+1 16 13 81.3 0.79 0.71 1.09 -0.50 2.20 -1.17 9.25 7.50 0.97 3.27
t+2 16 12 75.0 0.83 0.58 1.30 -0.58 2.26 -1.05 5.17 4.11 1.15 2.88
t+3 16 13 81.3 1.05 0.99 1.50 -0.87 1.71 -1.40 7.08 5.77 1.30 3.24
t+4 16 12 75.0 1.53 1.74 2.23 -0.54 4.10 -0.84 11.30 9.48 1.71 3.58
t+5 16 13 81.3 1.85 1.84 2.46 -0.79 3.13 -1.29 12.18 10.11 2.17 3.42
t+10 16 14 87.5 2.18 2.36 2.72 -1.63 1.67 -2.74 17.08 14.32 2.11 4.11
t+20 16 11 68.8 2.24 2.54 4.01 -1.67 2.40 -4.28 5.75 4.48 3.27 2.74
1st +’ve exit in 5 days 16 16 100.0 0.94 0.71 0.94 INF INF 0.04 NA NA 0.79 4.76

5) $XLF trading odds from Tax day’s close (Financial Select Sector SPDR ETF)

Exit # Wins % Wins Avg% Med% Avg Win % Avg Loss % Pay Off Max Loss % PF OAPF STDEV T-Test
t+1 16 12 75.0 0.75 0.96 1.52 -1.56 0.98 -3.64 3.66 3.00 1.69 1.77
t+2 16 12 75.0 1.19 0.94 1.98 -1.18 1.68 -2.68 6.32 4.99 1.91 2.49
t+3 16 10 62.5 0.69 0.85 2.59 -2.47 1.05 -9.01 2.85 2.07 3.49 0.79
t+4 16 11 68.8 1.38 1.41 2.72 -1.55 1.75 -2.45 5.34 4.29 2.58 2.15
t+5 16 12 75.0 1.46 1.28 2.66 -2.15 1.24 -5.77 6.18 4.45 3.26 1.79
t+10 16 13 81.3 1.95 2.64 2.90 -2.15 1.35 -2.51 6.14 4.79 2.57 3.04
t+20 16 11 68.8 1.88 2.77 4.36 -3.57 1.22 -7.14 3.07 2.55 4.39 1.72
1st +’ve exit in 5 days 16 14 87.5 1.04 0.98 1.39 -1.42 0.98 -1.47 9.14 7.58 1.29 3.22

 

6) $XLE trading odds from Tax day’s close (Energy Select Sector SPDR ETF)

Exit # Wins % Wins Avg% Med% Avg Win % Avg Loss % Pay Off Max Loss % PF OAPF STDEV T-Test
t+1 16 12 75.0 0.93 0.90 1.54 -0.91 1.70 -1.69 5.65 4.07 1.39 2.66
t+2 16 11 68.8 1.59 1.89 2.74 -0.93 2.96 -1.75 4.77 3.90 2.05 3.12
t+3 16 11 68.8 1.22 1.32 2.44 -1.44 1.69 -3.28 5.02 3.71 2.26 2.17
t+4 16 11 68.8 1.85 1.59 3.13 -0.98 3.20 -1.81 8.52 6.25 2.53 2.92
t+5 16 14 87.5 2.25 2.48 2.75 -1.27 2.17 -2.41 17.10 12.96 2.26 3.98
t+10 16 13 81.3 2.71 2.13 3.49 -0.70 4.99 -1.89 37.83 32.02 2.70 4.00
t+20 16 10 62.5 2.53 3.45 6.24 -3.65 1.71 -6.10 2.22 1.78 5.51 1.83
1st +’ve exit in 5 days 16 16 100.0 1.22 0.90 1.22 INF INF NA NA NA 1.01 4.82

7) $XLI trading odds from Tax day’s close ( Industrial Select Sector SPDR ETF)

Exit # Wins % Wins Avg% Med% Avg Win % Avg Loss % Pay Off Max Loss % PF OAPF STDEV T-Test
t+1 16 13 81.3 0.90 0.87 1.37 -1.14 1.20 -1.63 5.81 4.79 1.32 2.72
t+2 16 13 81.3 1.24 0.91 1.81 -1.24 1.46 -1.83 5.22 4.22 1.81 2.74
t+3 16 9 56.3 1.15 0.50 2.60 -0.71 3.64 -1.22 5.36 4.24 2.12 2.17
t+4 16 11 68.8 1.66 1.71 2.64 -0.49 5.42 -1.81 9.89 8.22 1.86 3.58
t+5 16 13 81.3 2.21 2.40 2.92 -0.83 3.50 -1.83 16.24 13.74 2.10 4.22
t+10 16 14 87.5 2.73 2.94 3.69 -4.03 0.92 -7.09 8.12 7.09 3.27 3.34
t+20 16 12 75.0 2.92 4.33 5.15 -3.76 1.37 -5.28 4.03 3.44 4.47 2.62
1st +’ve exit in 5 days 16 15 93.8 1.15 0.90 1.26 -0.52 2.43 -0.52 29.17 24.33 1.00 4.60

8) $IYR trading odds from Tax day’s close ( iShares US Real Estate  )

Exit # Wins % Wins Avg% Med% Avg Win % Avg Loss % Pay Off Max Loss % PF OAPF STDEV T-Test
t+1 14 10 71.4 0.80 0.50 1.58 -1.15 1.37 -2.09 3.67 2.50 1.78 1.68
t+2 14 10 71.4 1.11 1.23 2.19 -1.61 1.36 -2.91 3.29 2.34 2.28 1.82
t+3 14 9 64.3 0.44 0.60 1.95 -2.27 0.86 -6.13 2.57 1.75 2.71 0.61
t+4 14 11 78.6 1.35 1.59 2.21 -1.80 1.23 -2.99 6.52 5.29 1.97 2.56
t+5 14 11 78.6 1.19 1.00 2.04 -1.92 1.06 -4.12 6.97 5.51 2.15 2.07
t+10 14 11 78.6 2.34 3.19 3.94 -3.53 1.12 -5.38 4.96 4.15 3.65 2.40
t+20 14 11 78.6 1.73 3.05 3.48 -4.69 0.74 -5.66 3.35 2.74 3.65 1.77
1st +’ve exit in 5 days 14 13 92.9 1.04 0.75 1.44 -4.12 0.35 -4.12 7.89 5.82 1.92 2.03

9) $XLP trading odds from Tax day’s close ( Consumer Staples Select Sector SPDR ETF)

Exit # Wins % Wins Avg% Med% Avg Win % Avg Loss % Pay Off Max Loss % PF OAPF STDEV T-Test
t+1 16 11 68.8 0.41 0.59 0.99 -0.86 1.15 -2.90 3.69 2.69 1.15 1.44
t+2 16 9 56.3 0.26 0.71 1.16 -0.90 1.28 -3.80 2.04 1.68 1.34 0.77
t+3 16 12 75.0 0.20 0.58 0.73 -1.40 0.52 -1.95 2.27 1.81 1.07 0.74
t+4 16 12 75.0 0.79 0.49 1.16 -0.34 3.40 -0.95 8.78 6.35 1.21 2.60
t+5 16 11 68.8 0.77 0.38 1.39 -0.61 2.27 -1.07 5.24 3.78 1.35 2.26
t+10 16 10 62.5 0.86 0.79 1.94 -0.94 2.06 -2.34 4.60 3.64 1.64 2.09
t+20 16 10 62.5 1.44 0.53 2.93 -1.06 2.77 -2.02 4.96 3.99 2.49 2.30
1st +’ve exit in 5 days 16 14 87.5 0.69 0.66 0.89 -0.71 1.26 -1.07 12.58 9.63 0.76 3.66

10) $XLK trading odds from Tax day’s close (Technology Select Sector SPDR ETF )

Exit # Wins % Wins Avg% Med% Avg Win % Avg Loss % Pay Off Max Loss % PF OAPF STDEV T-Test
t+1 16 11 68.8 1.10 0.85 2.14 -1.19 1.80 -2.70 3.82 2.22 2.20 2.00
t+2 16 11 68.8 1.39 1.65 3.06 -2.28 1.34 -7.41 2.10 1.43 3.59 1.55
t+3 16 10 62.5 1.61 0.84 3.80 -2.04 1.86 -5.06 2.48 1.34 4.78 1.34
t+4 16 10 62.5 1.94 1.05 4.11 -1.66 2.48 -3.00 2.65 1.53 4.44 1.75
t+5 16 13 81.3 2.21 1.36 3.05 -1.43 2.13 -3.18 7.28 5.57 3.03 2.91
t+10 16 10 62.5 2.24 2.01 4.59 -1.69 2.72 -5.90 5.82 4.46 4.00 2.24
t+20 16 11 68.8 1.98 2.43 4.96 -4.56 1.09 -5.78 2.41 1.97 5.34 1.49
1st +’ve exit in 5 days 16 14 87.5 1.42 1.01 1.86 -1.66 1.12 -3.18 7.24 4.54 2.06 2.76

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Callan–Symanzik equation and a perfect 100 % record in predicting major crashes on $SPX

Callan–Symanzik equation applicability to predict major $SPX crashes

Callan–Symanzik equation

as we all know , in quantum electrodynamics the Callan–Symanzik equation takes the form

\left[M\frac{\partial }{\partial M}+\beta(e)\frac{\partial }{\partial e}+n\gamma_2 +m\gamma_3\right]G^{(n,m)}(x_1,x_2,\ldots,x_n;M,e)=0

being n and m the number of electrons and photons respectively.

modifying n for “Wicksellian interest rate” ( refer to Ben S. Bernanke ‘s blog post  Why are interest rates so low? dated  March 30, 2015 6:01am, , to understand the dynamics of Wicksellian interest rate )

, and m for the 

 , i’e the time elapsed between the worst 20 day percentage changes in $SPX ( S&P 500 cash index) , 

note  is the golden ratio multiplied by the prior $SPX crash date expressed in UNIX time-stamp format.

Below were the prior instances where the above modified Callan–Symanzik equation predicted the $SPX upcoming crashes in percentage terms ( 20-day non interleaving ) , since 1950 , mind you it has a perfect 100 % record thus far …

Date $SPX t+20%
31-Mar-16 ~2066 -??
11-Jul-11 1319.49 -15.16
28-Jan-09 874.09 -13.87
31-Oct-08 968.75 -15.75
29-Sep-08 1106.42 -23.27
29-Aug-08 1282.83 -13.75
26-Aug-02 947.95 -13.57
17-Jun-02 1036.17 -13.05
15-Aug-01 1178.02 -13.75
15-Feb-01 1326.61 -13.27
03-Aug-98 1112.44 -13.95
26-Jul-90 355.91 -13.73
21-Sep-87 310.54 -27.6
07-Nov-74 75.21 -13.56
24-Jul-74 84.99 -13.51
11-Jun-74 92.28 -13.32
26-Oct-73 111.38 -13.29
23-Apr-70 83.04 -13.1
30-Apr-62 65.24 -14.93

t+20 % , is the change in the next 20 trading days in percentage terms ..

ps: APR-FOOL 🙂 

ps: #FWIW the listed dates above are the worst 20-day non-interleaving performances for $SPX

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

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Trading Odds for August Opex Monday’s

Trading Odds for August Opex Monday’s

Option Expiraton

Below the trading odds for the $SPY longs on August Opex Monday’s since 1993 , assuming one had gone long on Friday at close and exiting on Monday at close

  • Winners : 15
  • Losers : 6
  • % Winners : 71%
  • Average Change % : 0.43
  • Median Change % : 0.44
  • Maximum Gain % : 2.13
  • Maximum Loss % : -2.46
  • Average Gain %if Winner : 0.83
  • Average Loss % if Loser : -0.58
  • Payoff Ratio 1.44
  • Profit Factor : 3.52
  • Outlier Adjusted Profit Factor : 2.76

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

below the trading odds for the $SPY longs for 1/2/3/4/5 trading days holding period , with entry set at the second Friday of the August ( that is , data since 1993

Exit # Wins % Wins Avg% Med% Avg Win % Avg Loss % Pay Off Max Loss % PF OAPF
t+1 21 15 71.4 0.43 0.44 0.83 -0.58 1.44 -2.46 3.52 2.76
t+2 21 12 57.1 0.39 0.68 1.28 -0.79 1.62 -2.54 2.09 1.58
t+3 21 14 66.7 0.50 0.62 1.25 -1.01 1.24 -2.53 1.91 1.51
t+4 21 13 61.9 0.36 0.20 1.31 -1.19 1.10 -3.06 1.27 1.04
t+5 21 14 66.7 0.35 0.93 1.51 -1.98 0.76 -4.63 1.24 1.02
1st +’ve exit in 5 days 21 21 100.0 0.75 0.68 0.75 INF INF 0.02 NA NA

21/21 times $SPY closed higher at some point of time in the next five trading days , with an average gain of 0.75% , for the longs entered on close 2nd Friday of August .

Below the historical instances of $SPY returns over the next 1-5 trading day’s , with the entry set to the 2nd Friday of August , since Feb 1993

Date $SPY t+1% t+2% t+3% t+4% t+5%
08-Aug-14 193.24 ?? ?? ?? ?? ??
09-Aug-13 166.05 -0.12 0.17 -0.34 -1.73 -2.06
10-Aug-12 135.11 -0.05 -0.04 0.07 0.81 0.95
12-Aug-11 110.95 2.12 1.24 1.32 -3.06 -4.63
13-Aug-10 99.74 -0.05 1.18 1.36 -0.4 -0.72
14-Aug-09 91.07 -2.46 -1.69 -0.82 0.2 2.16
08-Aug-08 113.82 1.04 -0.02 -0.62 0.13 0.62
10-Aug-07 124.8 0.36 -1.17 -2.53 -1.8 0
11-Aug-06 107.56 0.08 1.27 2.12 2.38 2.9
12-Aug-05 102.37 0.62 -0.69 -0.7 -0.71 -0.49
13-Aug-04 87.42 1.03 1.6 2.64 2.35 3.07
08-Aug-03 78.9 0.38 1.29 0.77 1.05 1.37
09-Aug-02 72.06 -0.74 -2.54 1.01 2.41 2.11
10-Aug-01 92.89 0.02 -0.01 -0.88 -0.54 -2.13
11-Aug-00 113.52 1.27 1.19 0.83 1.89 1.55
13-Aug-99 101.51 0.44 1.1 0.37 -0.44 0.56
14-Aug-98 79.91 2.13 4 3.74 3.13 2.3
08-Aug-97 69.36 0.74 -0.88 -1.17 -0.81 -3.85
09-Aug-96 48.32 0.75 -0.06 0.29 0.1 0.95
11-Aug-95 39.76 0.91 0.7 0.98 0.83 0.93
12-Aug-94 32.27 -0.03 0.68 0.62 0.28 0.22
13-Aug-93 30.65 0.55 0.88 1.44 1.44 1.5

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when $SPX breaks the yearly R1 pivot point

$SPX breaks the yearly R1 pivot point

$SPX R1 pivot point

h/t@Jeff York 

below the instances of $SPX prior year’s , since 1951 , crossing the yearly R1 Pivot Point and the returns from the R1 pivot point till the year end close.

R1 Pivot is calculated as R1 = 2*( previous year’s high + low + close)/3 – previous year’s low , for 2104 that value is 1989.8 , and as on  24th July 2014 , with the intra-day high of 1991.39 , we broken out of the yearly R1 pivot point

Below the trading odds for the $SPX longs , assuming one goes long after R1 pivot point is breached, at the R1 value , with the exit set to , year end close , ( i.e last trading day of the Dec of that year) , minus the complications of annual-zing the returns , as in certain years R1 is breached as early as in January itself , and for certain years it is breached as late as in November.

  • Winners : 31
  • Losers : 7
  • % Winners : 82%
  • Average Change % : 8.13
  • Median Change % : 6.32
  • Maximum Gain % : 34.55
  • Maximum Loss % : -8.80
  • Average Gain %if Winner : 10.86
  • Average Loss % if Loser : -3.98
  • Payoff Ratio 2.73

vs the buy and hold of $SPX from each year end close and holding on to the next year end close , since 1951

  • Winners : 46
  • Losers : 17
  • % Winners : 73%
  • Average Change % : 8.82
  • Median Change % : 11.78
  • Maximum Gain % : 45.02
  • Maximum Loss % : -38.49
  • Average Gain %if Winner : 16.89
  • Average Loss % if Loser : -13.04
  • Payoff Ratio 1.30

what did we achieve? -> our win rate and the pay off ratio ( average winner divided by average loss ) are slightly increased vs the buy and hold strategy !

below the historical details of $SPX breaking out R1 pivot point , and the year ending close details , since 1951

Date Month High Yearly R1 Year Close Cls – R1 Cls – R1 %
Jul-2014 1991.39 1989.8 ?? ?? ??
Feb-2013 1530.94 1514.18 1848.36 334.18 22.07
Mar-2012 1419.15 1393.86 1426.19 32.33 2.32
Feb-2011 1344.07 1343.19 1257.6 -85.59 -6.37
Apr-2007 1498.02 1493.64 1468.36 -25.28 -1.69
Mar-2006 1310.88 1304.01 1418.3 114.29 8.76
Nov-2005 1270.64 1265.93 1248.29 -17.64 -1.39
Apr-1999 1371.56 1345.16 1469.25 124.09 9.22
Mar-1998 1113.07 1061.27 1229.23 167.96 15.83
Feb-1997 817.68 802.81 970.43 167.62 20.88
May-1996 681.1 673.47 740.74 67.27 9.99
Feb-1995 489.19 482.79 615.93 133.14 27.58
Mar-1993 456.76 454.77 466.45 11.68 2.57
Feb-1991 370.96 368.5 417.09 48.59 13.19
Jan-1989 297.51 294.39 353.4 59.01 20.04
Jan-1987 280.96 263.83 247.08 -16.75 -6.35
Mar-1986 240.11 228.45 242.17 13.72 6.01
Jan-1985 180.27 176.01 211.28 35.27 20.04
Apr-1983 164.43 155.93 164.93 9 5.77
Oct-1982 140.4 138.74 140.64 1.9 1.37
Jan-1980 117.17 114.99 135.76 20.77 18.06
Aug-1979 109.84 107.29 107.94 0.65 0.61
Aug-1978 106.27 105.38 96.11 -9.27 -8.8
Jan-1976 101.99 101.63 107.46 5.83 5.74
May-1975 93.51 92.75 90.19 -2.56 -2.76
Mar-1972 109.75 108.68 118.05 9.37 8.62
Mar-1971 102.03 101.54 102.09 0.55 0.54
Jul-1968 103.67 103.38 103.86 0.48 0.46
Apr-1967 94.77 92.61 96.47 3.86 4.17
Apr-1965 89.64 89.43 92.43 3 3.35
Mar-1964 79.89 79.48 84.75 5.27 6.63
Sep-1963 73.87 73.38 75.02 1.64 2.23
Jan-1961 61.97 61.6 71.55 9.95 16.15
Jul-1959 60.62 60.17 59.89 -0.28 -0.47
Jul-1958 47.19 46.42 55.21 8.79 18.94
Jun-1955 41.03 39.71 45.48 5.77 14.53
Mar-1954 26.94 26.74 35.98 9.24 34.55
Jun-1952 24.96 24.85 26.57 1.72 6.92
Jan-1951 21.74 21.69 23.77 2.08 9.59

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$SPY $DIA $QQQ $IWM $GLD $TLT on July Opex days

$SPY $DIA $QQQ $IWM $GLD $TLT on July Opex days

Expiration   $SPY trading odds for $SPY longs on July Option expiry day , since 1993

  • Winners : 8
  • Losers : 13
  • % Winners : 38%
  • Average Change % : -0.51
  • Median Change % : -0.13
  • Maximum Gain % : 1.09
  • Maximum Loss % : -3.52
  • Average Gain %if Winner : 0.53
  • Average Loss % if Loser : -1.15
  • Payoff Ratio 0.46
  • Profit Factor : 0.31
  • Outlier Adjusted Profit Factor : 0.24

$QQQ trading odds for $QQQ longs on July Option expiry day , since 1993

  • Winners : 5
  • Losers : 10
  • % Winners : 33%
  • Average Change % : -1.03
  • Median Change % : -1.07
  • Maximum Gain % : 1.33
  • Maximum Loss % : -3.37
  • Average Gain %if Winner : 0.68
  • Average Loss % if Loser : -1.89
  • Payoff Ratio 0.36
  • Profit Factor : 0.17
  • Outlier Adjusted Profit Factor : 0.10

$IWM trading odds for $IWM longs on July Option expiry day , since 2000 ( or since QQQ started trading)

  • Winners : 5
  • Losers : 9
  • % Winners : 36%
  • Average Change % : -0.85
  • Median Change % : -0.74
  • Maximum Gain % : 0.70
  • Maximum Loss % : -3.64
  • Average Gain %if Winner : 0.46
  • Average Loss % if Loser : -1.77
  • Payoff Ratio 0.26
  • Profit Factor : 0.18
  • Outlier Adjusted Profit Factor : 0.12

$DIA trading odds for $DIA longs on July Option expiry day , since 1999 ( or since QQQ started trading)

  • Winners : 6
  • Losers : 9
  • % Winners : 40%
  • Average Change % : -0.54
  • Median Change % : -0.04
  • Maximum Gain % : 1.36
  • Maximum Loss % : -4.43
  • Average Gain %if Winner : 0.59
  • Average Loss % if Loser : -1.28
  • Payoff Ratio 0.46
  • Profit Factor : 0.31
  • Outlier Adjusted Profit Factor : 0.21

$GLD trading odds for $GLD longs on July Option expiry day , since 2004

  • Winners : 5
  • Losers : 4
  • % Winners : 56%
  • Average Change % : 0.00
  • Median Change % : 0.19
  • Maximum Gain % : 0.89
  • Maximum Loss % : -1.33
  • Average Gain %if Winner : 0.54
  • Average Loss % if Loser : -0.69
  • Payoff Ratio 0.79
  • Profit Factor : 1.11
  • Outlier Adjusted Profit Factor : 0.67

$TLT trading odds for $TLT longs on July Option expiry day , since 2002

  • Winners : 7
  • Losers : 4
  • % Winners : 64%
  • Average Change % : 0.33
  • Median Change % : 0.27
  • Maximum Gain % : 1.58
  • Maximum Loss % : -1.55
  • Average Gain %if Winner : 0.85
  • Average Loss % if Loser : -0.58
  • Payoff Ratio 1.48
  • Profit Factor : 3.05
  • Outlier Adjusted Profit Factor : 2.09

conclusion , long $TLT and short $QQQ ??

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RSI Indicator with Martingale Position Sizing

The following article is a guest post from Jared Broad, CEO and Founder of QuantConnect. QuantConnect is an online browser-based back-testing platform for C# that allows you to test custom strategies over 15 years of historical intraday data. This article will be part of a new bi-monthly post by Jared with strategy examples for algorithmic trading.

Martingale is a bet sizing technique for increasing odds of winning at the expense of increased risk. The classic example is a coin flipping game where the gambler doubles his bet if he loses, in the hopes of making back any losses to break even. He will continue doubling his bet through subsequent losses until the bet breaks even. Once he returns to whole he continues betting with a unit bet. In theory with infinite capital and exactly 50-50 probability martingale can ensure the gambler will always return a profit.

Martingale portfolios typically display near perfect equity curves with dramatic, short term drawdowns.
Martingale portfolios typically display near perfect equity curves with dramatic, short term drawdowns.

Martingale position sizing is sometimes used in trading strategies without knowing its true risks. When implemented in reality traders have limited leverage, and the market win-loss probability fluctuates as losses can when the market is range-bound. It is a certainty that with a sufficient sample size eventually catastrophic loss does occur, it’s just a matter of when.

To demonstrate this we built a martingale position management algorithm, and backtest it on 15 years of data in QuantConnect to highlight the crashes.

To decide entry points we chose a the popular Relative Strength Index (RSI) indicator and shorted when it was over 70, signalling it was overbought. Conversely we entered the market long when it was less than 30, signalling oversold. Our entry and exit conditions were fairly arbitrary because we wanted to explore martingale position sizing more than RSI.

Once the algorithm was vested we monitor for a minimum profit gain, and on achieving the minimum profit-gain we exit the strategy locking in the profit.

In the event the algorithm reaches our maximum loss, we record the trade loss and double-invert the position as per martingale rules. The trade’s loss is now attached to a “loss-chain” parameter which serves as memory of this doubling-sequence. The minimum profit gain must also recoup the loss-chain before resetting to start again.

Our backtest result shows our implementation beating the absolute return of the SPY over a 15 year period, but it has greater volatility resulting in a lower Sharpe Ratio (0.4 vs 1.1 S&P). Interestingly it demonstrates the characteristic crashes of a martingale strategy, but since we have fixed leverage the crashes never rebound to form the perfect straight line.

There are many areas for experimentation to improve the strategy performance, such as using more intelligent entry and exit techniques, anti-martingale position sizing and adjusting our entry and loss targets based on market volatility. But I’ll leave that for you to explore!

Originally posted by Jared Broad on QuantConnect.

few bullish setups on $SPY on 26th -31st May 2014 weekly bar

bullish setups on $SPY on 26th-31st May 2014 weekly bar

$SPY weekly chart 26 May 2014

 

1) $SPY posts an unfilled full gap up on the weekly chart , since Feb 1993 

unfilled full gap up is , when the current weekly low is greater than previous weekly high .

below the trading odds for the $SPY longs for the next 1/2/3/4/8/13/26 trading weeks

Exit # Wins % Wins Avg% Med% Avg Win % Avg Loss % Pay Off Max Loss % PF OAPF
t+1 16 8 50.0 -0.03 0.12 1.09 -1.15 0.95 -3.45 1.08 0.82
t+2 16 10 62.5 0.01 0.35 1.44 -2.37 0.61 -8.53 1.12 0.71
t+3 16 7 43.8 0.57 -0.08 2.36 -0.82 2.88 -3.38 2.33 1.80
t+4 16 11 68.8 0.98 1.64 2.53 -2.41 1.05 -4.54 2.17 1.81
t+8 16 14 87.5 3.04 2.88 3.76 -1.97 1.91 -3.06 11.36 9.36
t+13 16 14 87.5 4.16 3.74 4.84 -0.64 7.62 -0.91 53.25 44.03
t+26 15 14 93.3 8.20 9.58 9.53 -10.43 0.91 -10.43 10.05 8.64
1st +’ve exit in 8 weeks 16 15 93.8 0.83 1.03 1.09 -3.06 0.35 -3.06 5.70 4.54

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

below the historical instances, of “$SPY posting an unfilled full gap up on weekly bar since Feb 1993”

Week Starting $SPY t+1% t+2% t+3% t+4% t+8%
27-May-14 192.68 ?? ?? ?? ?? ??
23-Dec-13 183.04 -0.52 0.16 -0.11 -2.7 0.02
16-Sep-13 169.05 -1.06 -1.07 -0.27 2.15 5.47
08-Jul-13 165.07 0.99 0.96 2.05 1.07 -0.88
11-Mar-13 152.75 -0.14 0.54 -0.43 1.91 4.86
10-Oct-11 116.28 1.14 4.92 2.37 3.34 2.85
07-Feb-11 124.47 1.07 -0.59 -0.48 -1.7 0.25
13-Sep-10 104.64 2.07 1.88 3.6 4.63 6.85
14-Jun-10 103.38 -3.45 -8.53 -3.38 -4.54 -3.06
09-Nov-09 99.99 -0.17 -0.04 1.27 1.36 5.08
16-Apr-07 128.23 0.62 1.55 1.51 2.7 3.44
13-Dec-04 99.04 1.11 1.2 -0.84 -1.01 1.11
23-Nov-98 90.74 -0.95 -1.99 -0.51 3.01 2.9
02-Feb-98 76.43 0.37 2 3.44 4.24 11.11
11-Sep-95 42.17 -0.21 0.09 -0.05 0.33 1.87
13-Mar-95 35.37 1.33 1.1 2.29 3.05 6.45
12-Apr-93 30.45 -2.66 -2 -1.31 -2.1 0.36

2) inspired by We Have Now Gone 8 Days, from Victor Niederhoffer , but on weekly bar , 

we have now gone 7 weeks , without a -0.1% decline ,below the trading odds for the $SPY longs for the next 1/2/3/4/8/13/26 trading weeks , when $SPY hasn’t fallen more than -0.1% for 7 or more weeks in row , since Feb 1993

Exit # Wins % Wins Avg% Med% Avg Win % Avg Loss % Pay Off Max Loss % PF OAPF
t+1 20 9 45.0 0.15 -0.09 1.20 -0.71 1.70 -1.56 1.13 0.80
t+2 20 11 55.0 0.75 0.05 1.97 -0.75 2.64 -1.60 2.76 2.23
t+3 20 16 80.0 1.48 1.08 2.07 -0.88 2.35 -1.49 6.65 5.65
t+4 20 17 85.0 1.87 1.88 2.42 -1.25 1.94 -2.54 12.23 10.78
t+8 20 15 75.0 2.64 2.01 3.90 -1.16 3.37 -2.60 7.31 6.24
t+13 20 18 90.0 3.42 3.10 4.07 -2.39 1.70 -4.78 11.28 8.86
t+26 19 14 73.7 3.97 5.47 6.89 -4.20 1.64 -6.46 4.58 3.82
1st +’ve exit in 8 weeks 20 20 100.0 1.60 1.20 1.60 NA NA NA NA NA

below the historical instances of $SPY not falling more than -0.1% on weekly bar for 7 or more weeks in row , since 1993

Date # NO declines of > -0.1% $SPY t+1% t+2% t+3% t+4% t+8%
27-May-14 7 192.68 ?? ?? ?? ?? ??
02-Dec-13 10 179.17 -1.56 0.89 2.16 1.63 -0.99
25-Nov-13 9 179.23 -0.03 -1.60 0.85 2.13 -0.63
18-Nov-13 8 179.04 0.11 0.07 -1.49 0.96 2.12
11-Nov-13 7 178.29 0.42 0.53 0.49 -1.08 2.83
11-Feb-13 7 148.44 -0.14 0.00 2.19 2.90 4.86
10-Jan-11 7 120.91 -0.72 -1.22 1.43 2.94 1.19
14-May-07 7 131.69 -0.62 0.96 -1.04 0.72 1.90
20-Jan-04 9 93.1 -0.83 0.02 0.61 0.40 -2.60
12-Jan-04 8 92.94 0.17 -0.66 0.19 0.79 -1.44
05-Jan-04 7 91.44 1.64 1.82 0.97 1.84 3.55
16-Mar-98 8 82.87 -0.23 2.47 1.19 2.20 1.05
09-Mar-98 7 80.54 2.89 2.66 5.44 4.12 4.06
16-Jun-97 10 66.86 -0.75 2.77 2.45 1.91 0.22
09-Jun-97 9 66.7 0.24 -0.51 3.01 2.70 4.48
02-Jun-97 8 64.22 3.86 4.11 3.33 6.99 10.34
27-May-97 7 63.31 1.44 5.35 5.61 4.82 10.85
14-Oct-96 7 52.49 -1.28 -0.88 3.09 3.94 2.93
11-Dec-95 7 44.88 -0.98 -0.51 -0.33 -2.54 6.51
07-Sep-93 8 31.67 -0.66 -0.73 0.06 -0.13 -0.13
30-Aug-93 7 31.65 0.06 -0.60 -0.66 0.13 1.64

huh ! what is overbought definition ?

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The Importance of Benchmarking – Measuring the Sell in May Strategy

The following article is a guest post from Jared Broad, CEO and Founder of QuantConnect. QuantConnect is an online browser-based back-testing platform for C# that allows you to test custom strategies over 15 years of historical intraday data. This article will be part of a new bi-monthly post by Jared on algorithmic trading for retail investors.

There are two different techniques for measuring your strategy performance; relative and absolute performance. Before you design your strategy its important to define your metrics for success. As you iterate through strategy ideas this will help you know where you need to improve.

An absolute return strategy aims to make a consistent steady return independent of market conditions. It might rely on assets which are not affected by the market volatility such as bonds. Strategies which trade long and short are easier to be designed for an absolute return.

The alternative method is to compare or “benchmark” your strategy a market index. The precise index or market representation you choose can vary but often funds choose the S&P500. With a relative return strategy reducing holdings to cash is a net gain relative to a downward market.

We’ve integrated this directly into the QuantConnect results panel so you can load a custom benchmark into your charts, to know how your strategy is performing relative to the major indices. To access it, click on the “Select Benchmark” menu in the Chart Options on the right hand side:
bench

To demonstrate the benchmarking functionality we’ve written the next video in the QuantConnect University series – Sell in May and Go Away. It is a demonstration for how to code up the sell in may strategy, and then we benchmark it to the S&P500 over the same period.

With this particular implementation we achieved the same returns with lower volatility, resulting in and overall better than market strategy.

This article was written by Jared Broad and originally appeared on QuantConnect.

$SPY returns during the prior week of memorial day

$SPY returns during the prior week of memorial day

 

below the $SPY returns on the Mon to Fri , during week prior to Memorial Day , since Feb 1993 ,

Date Mon Tue Wed Thu Fri
28-May-13 -0.07 0.17 -0.83 -0.29 -0.06
29-May-12 1.6 0.05 0.17 0.14 -0.22
31-May-11 -1.19 -0.08 0.32 0.4 0.41
25-May-10 -1.42 -0.51 -3.9 1.5 -1.29
26-May-09 3.04 -0.17 -0.51 -1.68 -0.15
27-May-08 0.09 -0.93 -1.61 0.26 -1.32
29-May-07 0.15 -0.06 -0.12 -0.97 0.55
30-May-06 -0.39 -0.43 0.16 1.14 0.57
31-May-05 0.39 0.02 -0.34 0.64 0.1
25-May-04 0.68 -0.26 0.05 0.4 0.17
27-May-03 -2.49 -0.11 0.4 0.92 0.14
28-May-02 -1.33 -1.1 0.57 1.02 -1.21
29-May-01 1.62 -0.26 -1.55 0.32 -1.18
30-May-00 -0.44 -1.92 1.83 -1.25 -0.25
25-May-99 -0.46 0.82 -0.4 -0.64 -1.78
26-May-98 -0.26 0.33 0.86 -0.39 -0.37
27-May-97 0.42 1.01 -0.27 -0.44 1.36
28-May-96 0.63 -0.06 0.84 -0.36 0.37
30-May-95 0.86 0.94 0 0 -0.93
31-May-94 -0.38 0.36 0.34 0.16 0.06
25-May-93 -0.01 1.65 0.67 -1.05 0.48
# 21 21 21 21 21
Wins 10 9 12 11 10
%Wins 47.60% 42.90% 57.10% 52.40% 47.60%
Avg  0.05 -0.03 -0.16 -0.01 -0.22
avg win 0.95 0.59 0.52 0.63 0.42
avg loss -0.77 -0.49 -1.06 -0.71 -0.8
payoff 1.23 1.21 0.49 0.88 0.53

as you can see there is no major edge , if seasonality alone is used to trade the $SPY , prior to memorial day

$SPY average returns during the week prior to Memorial holiday , since 1993 

 

$SPY returns during the prior week to Memorial Day , since 1993

 

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