Two things come to mind - if I'm teaching grandpa to suck egss here feel free to say so ;-)
Optimization - you need to ensure that you only ever use parameters for trading going forward which you have derived from preceding data. Out of sample testing that derives parameters from time x and then tests a data set from time x-y is not valid. You also need to ensure that you don't overoptimize. Look for graceful degradation when making slight changes to numeric parameters. Abrupt degradation on slight parameter changes is a warning sign.
The other consideration is whether your simulation accurately reflects the fills you will get in real life. if you assume that you hit the bid or take the offer every time you only need to consider market size. But if you enter and exit with limit orders you'll either be in a queue or get filled pro rata, and that makes a HUGE difference to results.
Optimization - you need to ensure that you only ever use parameters for trading going forward which you have derived from preceding data. Out of sample testing that derives parameters from time x and then tests a data set from time x-y is not valid. You also need to ensure that you don't overoptimize. Look for graceful degradation when making slight changes to numeric parameters. Abrupt degradation on slight parameter changes is a warning sign.
The other consideration is whether your simulation accurately reflects the fills you will get in real life. if you assume that you hit the bid or take the offer every time you only need to consider market size. But if you enter and exit with limit orders you'll either be in a queue or get filled pro rata, and that makes a HUGE difference to results.
Quote from stephencrowley:
My system is general. I use statistics to verify the underlyings are 'tradeable' and then determine the maxium theoretical profit and then optimize rules to get as close to the maximum theorotical amount as possible while also hedging and limiting drawdown as well.
