I examined the code and it is missing the part that differentiates expectancy from expectancy score. What the presented code gives is a modified expectancy. If we take expectancyTemp and multiply it by opportunity as presented by the article I linked to in a previous post in this thread, we would then have expectancy score.
I would love to give it a try and make changes, but I really, at this time, have no idea about how to use the testing platform thyat you use. I will get thereb though.
I have just run a bunch of tests on several strategies that I am playing with and have calculated SQN, expectancy score, and a few other metrics for those tests. I am in a quandry.
Theoretically, when looking at several strategies, there is one that is better than all the others when run over the same time frame for the same set of securities. But better in what regard? That is the question. I am afraid that the answer is in the eye of the beholder.
In this thread we have discussed expectancy, sqn, and expectancy score. Looking at these 3 metrics against my tested strategies, I have not found any correlation reflecting the goodness of any strategy. In other words, what is shown to be best by one indicator is not shown to be best by any other indicator.
So again, the question comes down to exactly what are we looking for to determine goodness?
We are all doing this to make money. So that must be a criteria - how much money is returned. But because we all invest different amounts, the dollars returned must be a percentage of dollars invested. And because we know that 2% returned over 2 days is better than 2% returned over more than 2 days, we can divide the % dollars returned by the time frame (either minutes or days depending on whether we day trade or not). Taking that a step further, because we all invest over different time frames, we can multiply the result by 365 (for non day traders) or 480 (for day traders) in order to better compare the results.
Performing all of this, we have the following equation
($ returned / $ invested) * (365 / days in market) or
($ returned / $ invested) * (480 / minutes in market)
I am trying to decide whether this metric is sufficient or whether we must also account for variability using standard deviation or some such in which case we coulod end up with the following
(($ returned / $ invested) / stdev) * (365 / days in market) or
(($ returned / $ invested) / stdev) * (480 / minutes in market)
Thanks for letting me theorize here. I needed some place to discuss it. I would appreciate any and all input.
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I believe it all comes down to one's personal preference.
Trader A may prefer a system that has a lower expectancy but also a lower variability over a system with a very high expectancy but with a larger variability of returns. Whereas Trader B may be looking for much higher returns and is willing to risk greater variability to get them.
Personally, I need to have an idea of the variability of the system I'm trading. That's why a metric which accounts for variability suits me better.
The chance a trade is ever exactly breakeven is extremely slim, if you include commission (which you should).
Your platform can be configured to include commission in the net profit so I advise you do this, and as long as your commission is not exactly the equal to a tick of profit, then there should never be an exactly breakeven trade.
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My understanding is that you do not count them as a win or a loss, but that you do not reduce the total trades. In other words, if you have 2 wins, 3 losses, a 4 breakevens, you would use 2 as the # of wins, 3 as the # of losses, and 9 as the # of total trades.
Of course, it should be a rare occurence to have a truly breakeven trade unless you are trading commissionless securities which would happen with mutual funds or some of the ETFs available through Fidelity.
Hi, I'm reading Van Tharp's "Trade Your Way to Financial Freedom". I don;t quite understand how the opportunity cost is calculated. Does anyone knows how he calculate it? The book seems to be measuring by number of opportunity per day. How does one calculate when he is doing forward testing?