1 What is curve fitting?

Curve fitting is the process of adjusting the parameters or inputs of a model or a system to make it fit the data better. For example, if you have a trading strategy that uses moving averages, you can tweak the length of the moving averages to optimize the signals and the returns. Curve fitting can help you find the optimal settings for your strategy, based on the historical data you have.

2 What are the benefits of curve fitting?

Curve fitting can be beneficial for optimization, as it can boost the accuracy and reliability of a model or system by reducing the error between the output and the data. Additionally, it can help to uncover new patterns or relationships in the data by exploring different combinations of parameters or inputs. It can also assist in fine-tuning a strategy by testing its sensitivity to various factors or scenarios.

3 What are the drawbacks of curve fitting?

Curve fitting can have some drawbacks for optimization, such as overfitting, which is when your model or system becomes too complex or specific to the data and loses its ability to generalize. This can lead to poor performance or high risk in real trading as your strategy may not capture changing market conditions. Curve fitting can also cause data snooping, which is when you use the same data to develop and test your model or system, potentially inflating performance metrics or statistics. Additionally, it may consume a lot of time and resources as you may need to run multiple tests or iterations to find the best fit and deal with the noise or randomness in the data.