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According to me, backtesting resumes to a validation process of the optimization method employed to fix parameters of your model.

In such case you can perform multiple out of sample backtests, each of one having a different out of sample-period devoted to the optimization of the parameters and a second period to test the strategy.For each testing period you input new (optimized) parameters in your trading strategy. Then by observing results you can see if your optimization (and your model) is stable and correct over a long horizon. Additionally, in doing so, your backtests are never based on "in sample" data and you eliminate the bias. It allows you to evaluate the stability of the optimization method and not of a particular scenario.

This methodology is called Walk forward optimization, you can read the following book to know more about it :

The Evaluation and Optimization of Trading Strategies, 2nd Edition by Robert Pardo ISBN: 978-0-470-12801-5 Wiley trading.

For a quick overview : [Wiki link][1].

Ps : I will not use simulated data based on another statistical model because it will add another uncertainty in the evaluation (i.e : is your statistical model correct ?) [1]: https://en.wikipedia.org/wiki/Walk_forward_optimization

According to me, backtesting resumes to a validation process of the optimization method employed to fix parameters of your model.

In such case you can perform multiple out of sample backtests, each of one having a different out of sample-period devoted to the optimization of the parameters and a second period to test the strategy.For each testing period you input new (optimized) parameters in your trading strategy. Then by observing results you can see if your optimization (and your model) is stable and correct over a long horizon. Additionally, in doing so, your backtests are never based on "in sample" data and you eliminate the bias. It allows you to evaluate the stability of the optimization method and not of a particular scenario.

This methodology is called Walk forward optimization, you can read the following book to know more about it :

The Evaluation and Optimization of Trading Strategies, 2nd Edition by Robert Pardo ISBN: 978-0-470-12801-5 Wiley trading.

For a quick overview : Wiki link.

Ps : I will not use simulated data based on another statistical model because it will add another uncertainty in the evaluation (i.e : is your statistical model correct ?)

According to me, backtesting resumes to a validation process of the optimization method employed to fix parameters of your model.

In such case you can perform multiple out of sample backtests, each of one having a different out of sample-period devoted to the optimization of the parameters and a second period to test the strategy. Then by observing results you can see if your optimization (and your model) is stable and correct over a long horizon. Additionally, in doing so, your backtests are never based on "in sample" data and you eliminate the bias. It allows you to evaluate the stability of the optimization method and not of a particular scenario.

This methodology is called Walk forward optimization, you can read the following book to know more about it :

The Evaluation and Optimization of Trading Strategies, 2nd Edition by Robert Pardo ISBN: 978-0-470-12801-5 Wiley trading.

For a quick overview : [Wiki link][1].

Ps : I will not use simulated data based on another statistical model because it will add another uncertainty in the evaluation (i.e : is your statistical model correct ?) [1]: https://en.wikipedia.org/wiki/Walk_forward_optimization

According to me, backtesting resumes to a validation process of the optimization method employed to fix parameters of your model.

In such case you can perform multiple out of sample backtests, each of one having a different out of sample-period devoted to the optimization of the parameters and a second period to test the strategy.For each testing period you input new (optimized) parameters in your trading strategy. Then by observing results you can see if your optimization (and your model) is stable and correct over a long horizon. Additionally, in doing so, your backtests are never based on "in sample" data and you eliminate the bias. It allows you to evaluate the stability of the optimization method and not of a particular scenario.

This methodology is called Walk forward optimization, you can read the following book to know more about it :

The Evaluation and Optimization of Trading Strategies, 2nd Edition by Robert Pardo ISBN: 978-0-470-12801-5 Wiley trading.

For a quick overview : [Wiki link][1].

Ps : I will not use simulated data based on another statistical model because it will add another uncertainty in the evaluation (i.e : is your statistical model correct ?) [1]: https://en.wikipedia.org/wiki/Walk_forward_optimization

According to me, backtesting resumes to a validation process of the optimization method employed to fix parameters of your model.

In such case you can perform multiple out of sample backtests, each of one having a different out of sample-period devoted to the optimization of the parameters and a second period to test the strategy. Then by observing results you can see if your optimization (and your model) is stable and correct over a long horizon. Additionally, in doing so, your backtests are never based on "in sample" data and you eliminate the bias. It allows you to evaluate the stability of the optimization method and not of a particular scenario.

This methodology is called Walk forward optimization, you can read the following book to know more about it :

The Evaluation and Optimization of Trading Strategies, 2nd Edition by Robert Pardo ISBN: 978-0-470-12801-5 Wiley trading.

For a quick overview : [Wiki link][1].

Ps : I will not use simulated data based on another statistical model because it will implies another uncertainty in the evaluation (i.e : is your statistical model correct ?) [1]: https://en.wikipedia.org/wiki/Walk_forward_optimization

According to me, backtesting resumes to a validation process of the optimization method employed to fix parameters of your model.

In such case you can perform multiple out of sample backtests, each of one having a different out of sample-period devoted to the optimization of the parameters and a second period to test the strategy. Then by observing results you can see if your optimization (and your model) is stable and correct over a long horizon. Additionally, in doing so, your backtests are never based on "in sample" data and you eliminate the bias. It allows you to evaluate the stability of the optimization method and not of a particular scenario.

This methodology is called Walk forward optimization, you can read the following book to know more about it :

The Evaluation and Optimization of Trading Strategies, 2nd Edition by Robert Pardo ISBN: 978-0-470-12801-5 Wiley trading.

For a quick overview : [Wiki link][1].

Ps : I will not use simulated data based on another statistical model because it will add another uncertainty in the evaluation (i.e : is your statistical model correct ?) [1]: https://en.wikipedia.org/wiki/Walk_forward_optimization