All audios from videos by Andrea Unger, the only 4-time World Trading Champion.
Want to learn more about systematic trading? Click here to watch the FREE Masterclass!Artificial Intelligence can write code, analyze vast amounts of information, and generate ideas in seconds. But can it also build a truly reliable Trading System that is ready to go live? Today, tools such as ChatGPT, Claude, and Gemini seem to offer enormous potential for trading. We can ask AI to develop a strategy, write the corresponding code, and even generate backtest results. However, this does not necessarily mean that the results are accurate.At Unger Academy, we have tested Artificial Intelligence for developing Trading Systems and tools designed to support traders in their work. The results were interesting, but they also revealed some significant limitations. In one of our first tests, for example, we asked AI to develop an intraday strategy for Crude Oil.The system returned seemingly accurate data and performance metrics. However, when we analyzed the results, we discovered that the backtest had been conducted using data that did not match the market's actual historical performance.This is precisely the problem: AI can produce convincing answers even when the information it uses is incorrect. In trading, relying on unverified results can be particularly dangerous. A seemingly profitable strategy could be based on inaccurate data, unrealistic trading conditions, or calculations that fail to account for commissions, slippage, and liquidity.This does not mean that AI in trading is useless. On the contrary, it can be an extremely powerful tool for generating ideas, writing and debugging code, automating certain tasks, and accelerating the development of new tools. However, using AI in trading correctly requires the skills needed to formulate the right prompts, verify the results, and identify potential errors. Moreover, a single Trading System, even a valid one, is not a complete trading solution. Building a more robust approach requires diversified strategies, reliable data, testing, validation, risk management, and a structured development process.Artificial Intelligence can therefore support traders and make their work more efficient, but it cannot replace knowledge, methodology, and experience. In this video, Andrea Unger, 4-Time World Trading Champion, explains what AI can currently do in trading, its main limitations, and how it can be used within a controlled and informed development process. Enjoy!
Want to learn more about systematic trading? Click here to watch the FREE Masterclass!What do some of the most legendary and profitable traders in history have in common? Talent? Intuition? The ability to predict where the markets are headed?The answer is very different from what you might expect. Many of history's greatest traders built their success by following clear rules, relying on method rather than emotions, and progressively reducing subjectivity in their trading decisions. This evolutionary process laid the foundations not only for mechanical trading but also for modern systematic trading. Of course, not all of them did it in the same way. Nicolas Darvas was a professional dancer who traveled the world for work. Ed Seykota is considered one of the pioneers of modern systematic trading. The Turtle Traders, on the other hand, were ordinary people selected to take part in one of the most famous experiments in trading history. What makes their stories particularly fascinating is that they did not trade in the same way. They operated in different eras, traded different markets, and used different tools and strategies, yet they all shared the same philosophy: building a set of clear rules and removing as much subjectivity as possible from the decision-making process. It is a principle that still represents one of the cornerstones of both mechanical trading and modern systematic trading. In this clip from Unger Academy Summit Live 2026, Andrea revisits the stories of some of history's most celebrated traders to show how method, discipline, and risk management contributed to their extraordinary achievements. Enjoy !
Want to learn more about systematic trading? Click here to watch the FREE Masterclass! What are two former Formula 1 engineers doing in the Research & Development department of a trading academy? The answer has nothing to do with engines, race cars, or the glamour of the paddock... It's all about the mindset. In Formula 1, one wrong decision can cost millions of dollars. That's why there's no room for opinions, gut feelings, or the classic "I think."Every decision is based on data, testing, procedures, and analysis carried out by dozens of engineers. When a driver asks over the radio whether it's time to pit, nobody answers based on instinct. Behind that decision lies an enormous amount of research, simulation, and analysis completed long before the lights go out on race day.That's exactly the mindset that led us to bring Davide and Pierluigi into our Research & Development team. Both worked at the highest level of motorsport, contributing to the development of Formula 1 cars. Today, they apply that same scientific, data-driven approach to quantitative research in the financial markets.Of course, Formula 1 and trading are two completely different worlds. What they have in common, however, is the decision-making process. In both cases, success doesn't come from having the best intuition. It comes from building a system capable of turning data into objective, repeatable, and verifiable decisions.That is the philosophy behind our Research & Development team, and it's the same philosophy we strive to pass on to our students every day. In this clip from Unger Academy Summit Live 2026, Davide and Pierluigi share their professional journey, explain what really happens behind the scenes of a Formula 1 team, and reveal which principles from that world they still apply today in quantitative trading research.
Want to learn more about systematic trading? Click here to watch the FREE Masterclass! An IT technician working for a private company, Stefano has been passionate about the financial markets for more than ten years, dedicating his spare time to trading.For many years, he traded discretionarily. The problem, however, was always the same: keeping up with the markets, following the news, and staying on top of constantly changing market conditions. Periods of gains were inevitably followed by more difficult phases and inconsistent results, making trading increasingly stressful and frustrating.Determined to improve and continue learning, he encountered another major challenge: figuring out who to trust. With so many unrealistic promises and self-proclaimed experts, finding someone with real experience and a serious methodology was far from easy.Things changed when he discovered Andrea Unger and learned about his four World Cup Trading Championships victories. From that point on, he decided to dig deeper and embarked on a journey based on education, methodology, and practical application. His goal wasn't to find shortcuts, but to build a more solid approach and achieve more consistent results over time.Today, Stefano approaches the markets with greater confidence and peace of mind. He continues to study and is further diversifying his portfolio, adding options strategies alongside his traditional approaches. In this interview, he talks about:
Francesco is a commercial director for a multinational energy company and, above all, an engineer accustomed to making decisions in complex and uncertain environments. For years, he maintained a strong interest in the financial markets, approaching trading mainly through discretionary investing and focusing primarily on stocks. At some point, however, he realized that something essential was missing: a method.How could he turn his interest in the markets, his research, and his analysis into a structured and repeatable process? That question eventually led him to Unger Academy:to learn a rules-based approach built on clear processesto understand why risk management comes before profit seekingto approach the markets with a methodology aligned with his engineering mindsetIn the interview, Francesco explains what attracted him to systematic trading, which aspects of the Unger Academy approach resonated most with him, and why he believes risk management is the true foundation of a professional approach to the markets. Enjoy the interview and happy trading!Want to learn more about systematic trading? Click here to watch the FREE Masterclass!
The end-of-month rally, also known as the "Turn of the Month Effect", is one of the most well-known seasonal anomalies in financial markets. According to numerous studies, stock and index returns tend to concentrate during the last trading days of the month and the first trading days of the new month. But does this effect still work today? In this podcast, we analyze more than 100 years of historical Dow Jones data, from 1910 to the present day, to determine whether the end-of-month rally has remained stable over time, how it behaved across different market regimes, and what may actually be causing this seasonal anomaly. The analysis is also divided into three major historical periods of roughly 40 years each, allowing us to evaluate the long-term statistical robustness of the phenomenon. We will also discuss:buy and hold vs seasonal strategydrawdowns and risk managementhistorical outliers such as 1929, 2008, and 9/11institutional flows and capital inflowspossible explanations behind the Turn of the Month EffectEnjoy listening and happy trading!
Roberto, an accountant with a strong passion for financial markets, spent years trading discretionarily, focusing mainly on individual stocks without following a truly structured method. Like many traders, this approach led to highly inconsistent results: profitable periods alternated with more difficult phases, including significant losses. Over time, the frustration became so intense that he nearly stepped away from trading altogether.Then something changed. Roberto decided to explore a different approach, built on data, clear rules, and structured processes, moving closer to the world of systematic trading. And from that point on, not only did his results begin to change. His entire way of approaching the markets changed as well: "Today, I no longer invest because someone recommended a stock to me or because I heard a piece of news. I look for data, analysis, and concrete results."In this testimonial, Roberto shares his journey, his transition from discretionary trading to a more structured approach, and the changes that helped him achieve an overall return close to +100% in approximately 18 months. Watch the full video interview below... enjoy listening and happy trading!
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:
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!
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