I should nothing to add to your comment, as you have all said it.
If there is a shift in a market paradigm, then it does not make sense to include old data. As markets change all the time, a sample period, which is too long uses data which can no longer be exploited.
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I don't use dice for trading (though there were times when I would have done better if I did ).
The dice part refers to my addiction to backgammon.
While we're at it, it may be interesting to see what the success/failure distinction is in fields other than trading. What % separates losers, good players, and great players???
So let's take backgammon. There's an objective way to measure backgammon skill. The programs play at 'God' level or almost there, so their play / analysis is considered standard.
Here are the levels with error %s. They represent erroneous deviation from perfect play on each move.
Supernatural 0 to 5 %
World class 5 to 10 %
Expert 10 to 15 %
Advanced 15 to 20 %
Intermediate 20 to 25 %
Beginner 25 to 30 %
These are cumulative for each game. By that I mean, just because you play a perfect move, doesn't mean you play a perfect game. (Analogously, just because you make a perfect trade, doesn't mean you are a perfect trader). So an expert deviated 10 to 15% on all moves combined (effectively, maybe made 2-3 good but less than perfect plays).
As you can see, in backgammon, the differences between levels are about 5%.
From experience, I can play a game at the supernatural level occasionally, but certainly not consistently. If I average out my games, I perform somewhere between Advanced and Expert level in backgammon.
How much study and experience would it take to move into World Class?
A lot!! It's a not a linear curve.
I feel that roughly the same divisions are present in trading.
Advanced level is tough to achieve, but to go beyond that takes even more effort.
Same in other fields. You can get good at chess, but to became an Expert, Master, and Grandmaster presents progressively tougher strata of achievement, maybe exponentially so.
Some of my conclusions: 5% or 7% improvements are huge, and they are harder to achieve the higher you go.
"...the degree to which you think you know, assume you know, or in any way need to know what is going to happen next, is equal to the degree to which you will fail as a trader." - Mark Douglas
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You are right, I was taking into consideration the (a) category of system risk. However, I did feel that I was building a plan for the (a1) type of risk. Loss of an edge will show up in W/L ratio and accuracy. So why not understand the sensitivities so that you can leverage down if the system statistics are in a period of decline.
The (a2,a3) types were also on my mind as the number of trades in the back-test was not huge. That same sensitivity understanding and leverage management applies here as well.
For the (b) type of risk, in my system...what are the odds that the system enters a trade and the next 3 days move against it 10%? That is my unquantifiable risks of ruin. Everything else should be manageable. Having only time stops puts a lot of risk in the realm of what is possible in the market.
I will still run the same type of analysis you mentioned for (b) type risk to see what kind of results I get. I will likely use back-test and real trades for the bootstrap. If this method tells me to use more leverage than I am, then I may just stay sub-optimally levered.
Can you provide the link to the website you used for your testing? I used to use it quite a lot, but have misplaced the link. For some reason, I thought it had gone dead a year ago or so, so never updated my bookmarks.
@Bau250: He was referring to a win rate of 60% in combination with a R-Multiple of 2:1. This would mean that if your risk is R, out of ten trades you would have 6 winning trades of 2R and 4 losing trades of 1R. Your average trade would then yield (6*2R - 4R)/10 = 0.8 R for a risk of 1 R. This is indeed difficult to achieve, and if at all with a system that trades every when and now.
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