Seasonals – SP500, Euro

Here’s the strategy:
Each month we buy at the Open of the first day of the month and sell at the close of the last day of the month.

Here’s the average profit loss for the S&P500 Etfs, SPY  (yahoo:SPY). Data from 1993.

This chart shows that for example if we bought every December @ the open and sold at the end of the month @the Close, we would average a 1.97% profit.
But is average profit, alone, a good indication of profit loss potential?

One way to look at this is his:
Mr. X., a french expat and a bon viveur, wakes up and just feels like gambling. He takes a trip to the nearby casino. We ‘ll call it the Casino “Royale”. He enters the lobby and is presented with 12 different slot machines.

Mr. Francois is the oldest employee of the Royale and has been there since the very beginning. His job is to record the history of those machines. How many times each paid off and how much. 
Mr. X and Francois are buddies. So Mr. X has access to Francois’ notes. 
Francois keeps telling Mr. X, that past does not guarantee the future: If machine #3 paid off 100% of the times it was played, does not mean that it will pay off the next one.
But if it did, if the winning rates and payoffs were “stable”, ie, we expected future statistics to be similar to past statistics, which machine should Mr. X choose and how much should he bet?
The answer to both is the Kelly criterion: f=(bp-q)/b

f* is the fraction of the current bankroll to wager;
b is the net odds received on the wager (“b to 1”); i.e., if you play $1, how much do I win/loose.
p is the probability of winning;
q is the probability of losing, which is 1 − p.

The bigger the probability of winning and the bigger the money paid per $1 bet, the bigger Kelly becomes. . Which only makes sense since we want to play games with big winning percentages and large payoffs. So we can use Kelly as an “indicator” to tell us what would be our “best” bet.

So again, here’s the SPY chart using Kelly instead of Average Profit.

For December here are the stats:

NumOfwins[12]: 14     – i.e., 14 Decembers were wins
NumOfloss[12]: 6        – i.e., 6 Decembers were losers
AvgWin[12]: 2.6075
AvgLoss[12]: -2.02946
Certainty[12]: 70             – i.e. 14 winning Dec’s out of 20 = 70% winning prob.
EXPECT[12]: 1.21641
KELLY[12]: 0.466505

So now that we got all that out of the way, here’s a longer term chart for the SP500 (^GSPC) with data since 1960:
Here you can visually see where the “sell in May and go away” saying comes from. Again, this is historical stats. Doesn’t mean they predict the future.
Probably all this is known to you: The best months to have invested on the SPY are November and December and the worst are May to October. 
And why am I posting this now? It’s already the end of December…
So here’s another seasonal for my European friends that have lost much of their purchasing power over the last 2 years.
This is the Euro (using the Futures prices, since 1995). Keep in mind that the Euro has had a bullish bias through most of it’s history. But look at January:

Approximate continuous “averaged” EURO graph Jan. to Dec

Past returns may not be indicative of  future returns.

Connors RSI – Part 1

One of the readers of this blog, Mark, alerted me to a new indicator/system published from Connors/Alvarez : The ConnorsRSI.

What is the ConnorsRSI?

It consists of three components:
a. Short term Relative Strength, i.e., RSI(3).
b. Counting consecutive up and down days (streaks) and “normalizing” the data using RSI(streak,2). The result is a bounded, 0-100  indicator.
c. Magnitude of the move (percentage-wise) in relation to previous moves. This is measured using the percentRank() function.

The formula given is:
ConnorsRSI(3,2,100) = [ RSI(Close,3) + RSI(Streak,2) + PercentRank(percentMove,100) ] / 3

Bottom line: Connors/Alvarez have used similar indicators in the past. What is happening here is that they are creating a more robust indicator by averaging the three. They are “normalizing” the three indicators (rsi, consecutive moves and magnitude of move) to a 0-100 range and then averaging.

Connors/Alvarez propose a strategy that uses the Connors RSI coupled with other rules and filters on large section of U.S. Stocks.
In this post we’ll test the indicator on one security only, the SPY etf (as a proxy for the SP500).

Test A –
Instrument – SPY
1. Using ConnorsRSI(3,2,100) – We ‘ll call it Crsi.
2. Buy on Crsi<15
3. Sell on Crsi>70
4. Buy /Sell on the next bar Open price.
5. Commision of $0.005.

Test B –
The obvious question is whether the parameters are “fitted” to the data. What about using other parameters:
BuyThreshold –>5-50
BuyThreshold –>50-95

Test C –
What about before 1994? We ‘ll use ^GSPC (the SP500 Index).
Same parameters as “Test A”

Detailed results Test A

All trades
Initial capital 100000
Ending capital 336322.84
Net Profit 236322.84
Net Profit % 236.32%
Exposure % 6.55%
Net Risk Adjusted Return % 3606.42%
Annual Return % 6.30%
Risk Adjusted Return % 96.17%

All trades 179
Avg. Profit/Loss 1320.24
Avg. Profit/Loss % 0.73%
Avg. Bars Held 4.77

Winners 136 (75.98 %)
Total Profit 439687.37
Avg. Profit 3233
Avg. Profit % 1.71%
Avg. Bars Held 3.93
Max. Consecutive 11
Largest win 15964.53
# bars in largest win 3

Losers 43 (24.02 %)
Total Loss -203364.52
Avg. Loss -4729.41
Avg. Loss % -2.37%
Avg. Bars Held 7.42
Max. Consecutive 3
Largest loss -35059.29
# bars in largest loss 10

Max. trade drawdown -58157.34
Max. trade % drawdown -23.75%
Max. system drawdown -58157.34
Max. system % drawdown -23.28%
Recovery Factor 4.06
CAR/MaxDD 0.27
RAR/MaxDD 4.13
Profit Factor 2.16
Payoff Ratio 0.68
Standard Error 20256.9
Risk-Reward Ratio 0.5
Ulcer Index 3.5
Ulcer Performance Index 0.26
Sharpe Ratio of trades 2.23
K-Ratio 0.0404