487 Why Italy Will Become the Capital of Algorithmic Trading in 2026

492 After 20 Years in Forex, Manuela Switched to Trading Systems (for Her Daughters)

Manuela has been a professional Forex trader for more than two decades, and for years she traded discretionally, spending hours in front of charts every single day. Then an important question came up: how could she turn all that experience into something more solid, repeatable... and above all, transferable? That’s what led Manuela to Unger Academy: 👉 to validate her strategies through backtesting and real market data 👉 to automate part of her work and free up more time 👉 to build something that could become a real legacy for her daughters Over time, she deepened her knowledge of automated strategy development and also joined individual coaching sessions to work directly with the coaches on transforming her trading ideas into concrete systems. But there’s another aspect of her story that really stood out: the shared passion within a community of skilled traders, the continuous exchange of ideas, and the feeling of growing together with people who share the same serious and professional approach. Enjoy the interview and happy trading! Want to learn more about systematic trading? Click here to watch the FREE Masterclass!  [https://autc.pro/TSSeng-pod?utm_source=spreaker&utm_medium=poden&utm_content=492&sl=spreaker-poden-492]

491 Why Do Perfect Trading Strategies Fail Live The Problem of Overfitting

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! [https://autc.pro/TSSeng-pod?utm_source=spreaker&utm_medium=poden&utm_content=491&sl=spreaker-poden-491]

490 At 77, He Develops Trading Systems: Adriano’s Journey Through Algorithms and Markets

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489 +45% in 2025: Giacomo’s Story Between Trading Systems and Options

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488 Trading 2026: New Trends, AI and Strategies That Actually Work

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