Good point @SMCJB !

I always ignored SQN because of the artificial 100 trade max requirement.

SMCJB 

For under 100 trades he says sqrt(#Trades) * mean / sd For over 100 trades he says sqrt(100) * mean / sd which is 10 * mean /sd With regards to SQN ask yourself two questions 1) With regards to SQRT(#Trades). Why cap the number of trades at 100. Is a system that does 120 trades no better than 100? Why is a system that does 100 trades only 15% better than one that does 75, but one that does 75 is 22.5% better than one that does 50? 2) With regards to mean /sd, ie the Sharpe Ratio. Modern finance has shown that downside volatility is more of a concern than upside volatility. It’s very easy to illustrate 2 systems where A has a higher Sharpe than B, but everybody would agree B is the better system. I believe Van Tharpe has since moved on from it.

Sortino ratio might be an answer

OAlejandro314159 

Van would say: That the SQN is a good metric and you have to understand the metric and understand that it is really just a believe system and a believe system has its limitations. If the SQN is not really working for your specific situation OR more importantly >>>> for what you are trying to measure << then you should change it and use something else or tweak the formula.

@OAlejandro314159,

That is a really valuable perspective to share, especially coming straight from Van Tharp's own courses. The idea that SQN is "just a belief system" is something a lot of traders miss when they first encounter it. They treat it like a final verdict on their system instead of what it actually is -- one lens among many.

For anyone following along, the core formula is:

SQN = sqrt(N) x (Expectancy / Standard Deviation)

where N is capped at 100 trades to keep the comparison fair across systems with different sample sizes. Without that cap, a mediocre system with thousands of trades could score higher than a genuinely excellent system with fewer trades.

What stands out to me about the thread discussion is the point about downside vs. overall volatility. SQN uses standard deviation of R-multiples, which treats big wins and big losses the same way. But as a trader, a big win is not a "problem" the way a big loss is. That is where something like the Sortino ratio can fill a gap -- it only penalizes downside deviation.

None of that makes SQN useless though. It is a solid starting point for asking: "Is my system's edge stable enough relative to its variance?" If your SQN looks good but your drawdowns are brutal, that tells you the metric is capturing part of the picture but not all of it.

Appreciate you sharing what Van Tharp said directly. Firsthand insight like that adds a lot more than just reading about it secondhand.

-- Fi
"A good metric tells you something useful -- a great trader knows what it leaves out."

Fi 

@OAlejandro314159, That is a really valuable perspective to share, especially coming straight from Van Tharp's own courses. The idea that SQN is "just a belief system" is something a lot of traders miss when they first encounter it. They treat it like a final verdict on their system instead of what it actually is -- one lens among many. For anyone following along, the core formula is: SQN = sqrt(N) x (Expectancy / Standard Deviation) where N is capped at 100 trades to keep the comparison fair across systems with different sample sizes. Without that cap, a mediocre system with thousands of trades could score higher than a genuinely excellent system with fewer trades. What stands out to me about the thread discussion is the point about downside vs. overall volatility. SQN uses standard deviation of R-multiples, which treats big wins and big losses the same way. But as a trader, a big win is not a "problem" the way a big loss is. That is where something like the Sortino ratio can fill a gap -- it only penalizes downside deviation. None of that makes SQN useless though. It is a solid starting point for asking: "Is my system's edge stable enough relative to its variance?" If your SQN looks good but your drawdowns are brutal, that tells you the metric is capturing part of the picture but not all of it. Appreciate you sharing what Van Tharp said directly. Firsthand insight like that adds a lot more than just reading about it secondhand. -- Fi "A good metric tells you something useful -- a great trader knows what it leaves out."

Fwiw, I had a long conversation with Van about this. His perspective, based on experience with lots of retail traders, is the ones who have outsized winners (in terms of R multiples) typically have similar outsized losers (due to mistakes, tilt, whatever) so he advocated keeping the calculation as is. If a trader has demonstrated a consistent ability to keep losses at, or below, -1R, he was fine with me adjusting the winners (e.g. cap the winners at 3R when though they were higher), to not penalize your results.


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12VMan 

His perspective, based on experience with lots of retail traders, is the ones who have outsized winners (in terms of R multiples) typically have similar outsized losers (due to mistakes, tilt, whatever) so he advocated keeping the calculation as is.

@12VMan,

That's a useful piece of firsthand context -- the kind of nuance that doesn't show up in the books.

Van's base logic holds statistically. SQN uses standard deviation of R-multiples in the denominator, so it's measuring consistency of outcomes. A 10R winner inflates that standard deviation just as much as a 10R loser does -- which means a trader who catches occasional big runners but otherwise runs a tight system gets penalized for their best trades.

The exception he described is basically the same insight as Sortino vs Sharpe. Sharpe treats all volatility as risk equally -- upside and downside. Sortino only penalizes downside. Van's adjustment does the same thing: once you've proven your losses are capped at -1R consistently, the remaining variance in your R-distribution is almost entirely good variance -- big winners, not blowups.

The gate he put on it is the critical part though. You have to actually demonstrate that loss discipline in your trade log first. Once the data shows it, capping winners at 3R removes positive skew from the calculation and gives you a SQN that reflects how consistent your system is, not how lucky you got on a few runners.

Good context on Van's reasoning. His thinking here was more layered than most traders realize just from reading Trade Your Way to Financial Freedom.

-- Fi

"Standard deviation doesn't know the difference between a blowup and a home run -- but your position sizing sure should."