Found an article " Profitable ETF Trading Strategies: reflections on System Quality Number" @ http://kansasreflections.wordpress.com/2009/05/18/profitable-etf-trading-strategies-reflections-on-system-quality-number/
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here is my take on the IITM System Quality NUmber idea (SQN): and fat right tails. it’s what I said to chuck whitman on this topic
on my -1R loss exits: this is the result of a single trade decision cycle on that trade that is very effective
now, on the possibility of a 10R win that “skews” the histogram of results and increases the variability of the data set and therefore lowers SQN and therefore lowers recommended risk
the essential question is this: was the 20R a consequence of the act of entry, of the fact of entry, independent of any trader decisionmaking along the way?
if you have no influence over the achievement of 20R in the trade, then the argument that the increased variability suggested by the 10R return should reduce the SQN is warranted
because upside variability that is a result of the market’s “decision” ought to imply the potential of the same kind of downside variability suprising you
that is the essence of the marble game, that once you decide to play that the result is pure random function generator
BUT: what if you had played the washout pattern in SPY on 10 March and followed every rule and were able to manage the trade thru 6 iterations of successive Washout Cointinuation patterns and as a result of 7 cycles of trade decisions, were able to bring in 10R, yet never risked more than 1R on the downside
everything is now a function of how you classify that trade batch
if you say: that is one episode, and the trade is 10R, and the 10R implies i could have taken a 10R loss which is beyond my management ability, then i will say i disagree
if we have traded a WO pattern system 200 times and my 100 losses show an avg loss of less than 1R, AND my worst loss was 1.4R, and my trading mgt skills enabled me to manage risk properly all the way up the ladder, then simply treating the WOCs as 6 separate trades on avg of 1.6R (6x 1.6R = 10R) gives a true picture of the quality of the system
the Sortino ratio which examines the stats of just the losses with StDev is the right way to understand the losses and since computation is free you SHOULD do both the standard SQN AND a Sortino to better understand your loss pattern to make your decision on how much risk to take on
now, if i can get a 10R thru intraday trade mgt by getting a carefully engineered, risk controlled, very manageable morning hook which gives me a boost and my trade is a single continuous episode but Inever was in a position to experience a 10R loss, then you would be nuts to penalize that system
you MUST really know your edge, and where the 10R comes from and decide if it truly represents the possibility of expereincing a 10R loss
you MUST know your system thoroughly in order to create meaning, to fully understand its risks (as much as it can be understood)
portfolio heat rules and other heuristics must be in place to protect us against -10Rs beyond our control such as power outages, discontinuity in the mkts, so that we never commit the hubris error of LTCM
we are no where near that in our application
here is where SQN is VERY VALUABLE: when i have 5 sets of mechanical rules that i am testing, iindependent of trader discretion, as a check on the robustness of the mechanical framework
SQN can give me the basis for deciding which to pursue, to revise and extend
what your studies are revealing is that you are understanding how the SQN math works
this is a good thing
example: this morning in the seminar we looked at a Triple Screen on GOOG which if executed mechanically would have given a .8R win going in to the close, but which had “open risk” ie trader initial cpaital at risk AND was in the red for 5 hours until near the close
by applying trader Quality to it, we engineered the entry timing in order to get a 2R iStop, and scratched the 1st trade, and then earned 6R on the identical setup on the next leg up.
the open risk was 15 minutes, when we moved to no lose, and then we spent 4 hours in the green until deciding to cash a 5R before the close
i guarantee that the 5R win should not be interpretted as implying increasing downside volatility and thus lowering SQN
coming to that conclusion would demonstrate IN MY OPINION, an inferior understanding of interpreting SQN
let me suggest what true Quality measure could/should include: amount of open risk x # of minutes
1st GOOG example: 15 min red all of which was <1R and then all green rest of the day up to a 5R win
correction that was the 2d GOOG example: the 1st GOOG exmaple eas mostkly red all day = lots of pain, low quality; compare the “time area in the red” to the “time area in the green” to really understand the “quality of each system.
charts to follow
bottom line: you better know where your outsize R comes from
you must be ruthlessly focusing on your R losses to ensure you are calibrated in identifying mkt risk to your idea
you must be open to the potentials of achieving risk managed high R wins, ie fat right tails
the end
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The statistic is beyond me, but I felt that the FSB Pro should advice us on the risk based on the criteria above of the systems found so we reduces the risk and understand more of it.
