Basically I meant a system with some robust core (algorithm) which is not optimizable and which gives you some positive expectancy, in other words the quality ingredient. Then I'd be looking to enhance it with quantity gradient which can be optimized. So, in short, if you set not the best parameters you'd still get some profit in long run, maybe with fractional profit factor like 1.01 (well honestly in practice I would not go with 1.01 but maybe with 1.5). Then the goal of your optimizable parameters is to make the profit factor acceptable. If you made a poor choice with it or badly over-optimized it you'd still save you account. No matter how good a system could be I always expect it may fail at any time therefore a backup system should be at hands as well until you figure out how to fix the original system. That's what was mentioned here as observing, learning and adjusting in real time. Just my point of view derived from practice.
Thanks for explaining. I would never have guessed. Your "core" algorithm is indeed the holy grail, a basic concept so good that it has positive expectancy without optimization. The only ones I have ever seen were cured by looking at longer data histories. But I am still looking. I agree with you about backups. One should always have them. For women and systems.
Earnings trades are difficult... there are top-line and bottom-line expectations and even if a company beats or falls short, guidance can totally override the numbers. And then there's the market's mood at the moment. So you can lose even if you correctly forecast revenue and net. But you started off at a disadvantage because you "would rather wing it" when even the simplest analysis shows that the results of the three companies you relied on are useless for forecasting IBM's numbers.