For annual profit I would suggest systemPerformance.AllTrades.TradesPerformance.TradesPerDay * 365 * systemPerformance.AllTrades.TradesPerformance.Currency.CumProfit / systemPerformance.AllTrades.Count
(There may be better ways, that's without pulling up documentation or intellisense)
The following user says Thank You to fluxsmith for this post:
Sometimes I wonder if I go in circles, then I realize I haven't, which then makes me wonder even more .. I was using various 'optimization functions' available to NT7, and after a long day and night of jumping from one machine to another VM and back again (and seeing the same results too many times) I thought for sure I was ready for sleep so I did ... in the morning the results were still there ... blessed be: your Quality3.cs is giving me the same results, both on variables inputs AND $$/trades summary that the SQN.cs ( https://futures.io/free_downloads/ninjatrader/strategies/99-download.html?action=jump ) is producing ...
the only diff is the very nice 'performance' rating supplied by your code (4+ rating in a test of a long only strategy in a period of choppy bearish action)
now I dont know if I should be
Any thoughts ? I KNOW the code is different, just wondering if you had noticed the parallel results?
The following user says Thank You to Trader.Jon for this post:
They're based on the same principle, so they would often return the same scoring order of parameters. Mine does try to make some adjustments for conservatism, and annualizes the score, which is why on a backtest over less than a year you'll see higher scores on mine (and lower scores on a backtest over more than a year).
The following 4 users say Thank You to fluxsmith for this post:
why would it work better to try to backtest and optimize on a very long time frame - markets change all the time - i find that when i backtest for about 60-90 days and walkforward about 30 days it gives pretty good results overall for the next week or so to live run -
what i am still having a difficult time with is finding the right parameter set count - where a function or system will automaticlly calculate the population number and iterations it needs based on the parameter set count
I look at markets as having different personalities when they are bullish or bearish, so different parameters or even different straegies may have to be used.
Personally, I feel that it is better to optimize on the most demanding conditions: currently I am working on LONG ONLY(testing in choppy bearish time period) and SHORT ONLY(testing in choppy bullish time period) testing of strategies.