Multi-Variable supportive optimization can do the math for two or multi variables to evaluate what inputs would have achieved the best. For instance, traders can add some inputs to their strategy in the program, then these inputs need to be optimized according to their ideal weight using the tested historical data.
That’s why backtesting can be exciting in turning an unprofitable system into a money-making machine with a few optimizations. Yet, tweaking a trading system to make the highest level of profitability often leads to the system’s failure in real-life trading. This over optimisation of the system seems to look good on paper only.
Curve fitting is another optimization study that makes use of optimization analytics to point the highest numbers of winning trades based on past data of the testing period.
Backtesting and optimisation can be very fruitful to a trader, but all of this is only a part of the system in the evaluation of a potential trading system. A trader’s next step should be applying the system to historical data which has never been used in the initial phase of backtesting.