Auto-Pilot Disengaged – August 8, 2011

[Pascal]

Is the "POMO periods / ETF Confirmation" a case of correlation or a case of causation in the process of developing new robot rules (http://stats.org/faq_vs.htm)? I would feel quite more comfortable with the ETF confirmation rule if the rationale for its use during POMO periods was inferred based on logical grounds prior to a data mining exercise that shows the superior performance it produces.

Trader D

I introduced a confirmed short signal rule when I noticed at some point that large players were selling SDS while the 20DMF was issuing a short signal. I thus concluded that the short might be a false signal and overruled it at that time (early POMO days). It is on that base that I included the SDS confirmation in teh 20DMF rules - that was later on changed by the four inversed ETFs confirmation.

The important issue is not regarding the 20DMF. I think that the issue is to render the robot strong enough to sustain minimal damage in case of market direction error or even to overrule the 20DMF at some point. The solution I believe is in better sorting out the weak and strong robot trading signals. I am working on that. Another item is to revert to cash whenever the LT/ST signals are so completely apart - which indicates that the model has reached its programmed statistical limits. We are in such an exceptional situation right now.


Pascal

[Timothy Clontz]

Billy, Pascal,

Do I understand correctly that in the future the Robot will have occasional cash positions?

Tim

[Billy]

Billy, Pascal,

Do I understand correctly that in the future the Robot will have occasional cash positions?

Tim

Yes Tim,
This could be a possible new rule at times. We'll know after full backtesting of new suggested rules have been done.
Billy

[Timothy Clontz]

I've alo been considering a cash option for my sector selections. In my own backtesting the long sector outperforms the short sector (on average) even during drastic market moves. A long XLK hedged against a short XLE would have continued to make money even after being stopped out.

I'll work on my own ruleset to incorporate those kinds of conditions for the sector test. My own "neutral" setting may simply be hedged instead of in cash.

In such a condition a stop loss may only stop out half of the position and enter a stop limit entry point for the hedge.

[TraderD]

I introduced a confirmed short signal rule when I noticed at some point that large players were selling SDS while the 20DMF was issuing a short signal. I thus concluded that the short might be a false signal and overruled it at that time (early POMO days). It is on that base that I included the SDS confirmation in teh 20DMF rules - that was later on changed by the four inversed ETFs confirmation.

The important issue is not regarding the 20DMF. I think that the issue is to render the robot strong enough to sustain minimal damage in case of market direction error or even to overrule the 20DMF at some point. The solution I believe is in better sorting out the weak and strong robot trading signals. I am working on that. Another item is to revert to cash whenever the LT/ST signals are so completely apart - which indicates that the model has reached its programmed statistical limits. We are in such an exceptional situation right now.

Pascal

Pascal, thanks for the explanation.

My primary concern is the rule derivation methodology. In other words, how an anecdotal observation is validated to be statistically significant for inclusion in the robot's rule set.

From a parametrization standpoint, whatever ends up being selected has to: (a) be based on some apriori rationale, (b) coherently fit into the model and (c) have minimal dimensionality with respect to the available data on which it is intended to be validated. Clustering/segmentation of the LT/ST signals (e.g. weak/strong) seems at first glance to be a reasonable choice for satisfying (a)-(c) above.

Repeat analysis of the signals' distributions may be necessary every so often to re-align the model with changing dynamics of the market (POMO or otherwise). Another option is to utilize "range-adaptive" cluster boundaries that are automatically re-calculated based on a recent history (e.g. past 12 or 24 months) to compensate for the inevitable market drift. The latter often feels more "versatile" than relying on hard-coded thresholds.

Trader D