When it comes to systematic trading trading, one of the most common objections concerns the reliability of backtesting itself. Many people believe that trading systems only work on past market data and that the results achieved through historical testing have no real predictive value. And to some extent, that criticism makes sense. Achieving excellent backtest results is not particularly difficult: all you need to do is keep adapting rules, filters, and parameters to past market data until you create a strategy that appears almost perfect. The problem is that, in many cases, you are not actually measuring the true statistical effectiveness of the strategy, but simply how well it fits the historical data used to build it. And this is exactly where one of the most common mistakes in systematic strategy development comes into play: overfitting. Overfitting refers to the tendency to create systems that are too closely "tailored" to historical data and therefore tend to lose effectiveness once applied to real markets. So does this mean that backtesting is useless? Absolutely not. When used correctly, historical testing is an essential tool for evaluating the robustness of a strategy and verifying whether a market logic actually has a certain level of statistical validity. The key is understanding how to build robust, simple strategies based on realistic market concepts, while avoiding excessive optimization and overly specific parameters. In today’s episode, we will explore these aspects in depth, showing practical examples and discussing several useful techniques for developing trading systems with a higher probability of remaining effective in the future. Enjoy listening and happy trading! 😉Want to learn more about systematic trading? Click here to watch the FREE Masterclass!

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