fitness function for Genetic Algorithm
You'll perhaps get as many different ideas of 'successful' as members here.
Mine is 'preservation of capital' e.g. I'm not prepared to risk $100 to make $5 profit (that's how some methods can claim 'over 90% success rate')
Another factor to consider is sometimes called 'velocity' - how quicker the EA turns over its capital (related to how many times the EA trades) e.g. for the same risk, $10 profit 100 times a month is better than $100 profit 5 times a month, even though the second method has a higher 'profit factor'
I also like a method that is not too sensitive to 'tuning' e.g. If varying my SL from say 10 to 11 to 12 pips results in loss $1000 to gain $5000 to loss $2000, I'd be very suspicious of thinking that 11 pip SL is going to be the Holy Grail.
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Hi all,
I'm revising the fitness function of my genetic algorithm implementation and so far I've only had the profit to be the only criteria. That means the fittest lifeform/strategy is the one with the highest pip count.
Clearly this needs improvement. So for now I have:
- profit, in relation to the maximum profit of all lifeforms in the population
- maximum number of consecutive loosing positions, in relation to the maximum number of consecutive loosing positions of all lifeforms in the population
- maximum loss of consecutive loosing positions, in relation to the lifeform's profit
I would like to hear your opinion on that. What's your definition of a _successful_ automated strategy?