S&P 500 (and many other markets) finished the month of October in the red, down 1.8%. This downturn took the index within striking distance of its 10-month moving average, but wasn’t sufficient to penetrate it. Still, another 1.78% in November and the index will close below the 10-month moving average.
Another trade during this week, and again a winning one. A two day short position opened on Oct 19th and closed on Oct 23th, two trading days in total, 1.37% gain without leverage, about 2% with the leverage. Not bad at all. The long position opened on Oct 23th doesn’t look so good though.
The recent pull back in markets has brought the S&P 500 close to its long term moving averages. The 50-day is already broken, and the index is only about 2.5% above its 200-day moving average. This coming week is the end of the month, the time for an update on the 10- and 12-month moving averages. According to my computations, for the 10-month moving average to be broken, the S&P 500 (SPY) has to close below $137.72 on Wednesday. That’s more than 2% away from the Friday’s close, but certainly not out of reach.
An idea that I have been toying for a while, has been to study the effect of a domain-specific optimization strategy in the ARMA+GARCH models. If you recall from this long tutorial, the implemented approach cycles through all models within a the specified ranges for the parameters and chooses the best model based on the AIC statistic. One idea which I have studied recently is to try to improve the model selection by using a different criteria to determine the “best” model, namely to use a domain-specific strategy.
Here is where greed enters the picture: Since our domain is finance, and they claim greed is good. What if we choose the model which has best performance in-sample?
Lately, my S&P 500 system doesn’t want to stay short for too long. Switched from short to long at the close today, using a position size of about 1.5.
Friday was interesting – it was the first big down day in a while. The S&P 500 lost 1.67%. As the press reported, the last bigger drop in the S&P 500 was on June 21. The S&P 500 shed 2.23% on that day, but this was long time ago.
Since I am using both long and shorts (a big positive day could be as damaging as a big negative one) on the S&P 500, there is another piece of information which I am more interested: how far ago was the last bigger daily (positive or negative) return. The last bigger absolute daily return was on Sep 6, when the S&P 500 advanced 2.04%. This was also quite some time ago.
At the end, a single daily spike is nothing more but a good news material. What matters in practice is volatility and it’s yet to be seen whether it will pick up or not.
Back to trading. On the close of Friday, I switched from long to short on the S&P 500. This long was established on Oct 11 and was worth … 0%. In other words, all the gains evaporated during the Friday’s massacre. Nothing to worry though, we have seen worse, much worse. 🙂
Switched from long to short at the close today, using a position size of about 1.5. It was a close call, a few more points down and I would have kept the long.
More trading last week – my system signalled changes in the S&P 500 position twice. The first signal was at the Monday’s close, I switched from long to short. The long position was in place since Sep 25th. Extended by the leverage, the gain was 1.6%.
Then the following short lasted only until Thursday’s close, but the return was even better – 2.3%, again including the leverage.
Switched from short to long at the close today, using a position size of about 1.5.
Switched from long to short at the close today, using a position size of about 1.5.
After I came back from vacation, on Sep 17th, I resumed my trading by opening a short position on the SPY (S&P 500 ETF). This position was reversed on Sep 25th. The SPY lost about 1.8% during this period, which was a gain for me.
Due to low recent volatility in this ETF, the position was leveraged to 1.5, 50% larger than the pool I use for it. This extended the gains on the position to about 2.7%.