137 - The Scientific Method of Strategy Development w/ Quantitativo
Sep 26, 2024
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Carlos, a Brazilian quantitative trader and the mind behind the Quant Trading Rules blog, shares his journey from engineer to trader. He discusses innovative mean reversion strategies based on first principles and the importance of systematic strategy development through scientific methods. The conversation highlights the role of blogging in fostering community and knowledge-sharing among traders. Carlos also addresses the integration of machine learning in trading while balancing complexity, emphasizing the iterative nature of refining strategies.
Carlos emphasizes the significance of continuous learning and experimentation in developing innovative trading strategies through a methodical, first principles approach.
His blogging journey highlights the value of knowledge sharing in the trading community, fostering connections and fulfilling a demand for educational resources.
Deep dives
Background and Career Journey
The guest, Carlos, shares his journey from being an aerospace engineer to becoming a full-time trader and blogger. He began coding at a young age, which sparked his interest in technology, math, and ultimately trading. After founding and selling a technology company, he transitioned to the trading world, leveraging skills honed through machine learning. Carlos emphasizes the importance of continuous learning, particularly through his master's degree which included a focus on machine learning for trading applications.
Mean Reversion Strategy Development
Carlos discusses his article on mean reversion strategies, which focuses on deconstructing and building a new mean reversion indicator from scratch. This process includes critically analyzing the mechanics of mean reversion and statistically validating the effectiveness of the new indicator. He highlights how this methodical approach involves not only experimentation but also backtesting for statistical edge. By employing first principles thinking, he aims to create innovative trading strategies that are grounded in solid reasoning.
Implementing Machine Learning in Trading
In another article, Carlos explores the integration of machine learning in developing trading strategies, building on his previous research on mean reversion. He introduces a machine learning model designed to predict stock bounce-back probabilities after deviations from the mean. While acknowledging that more complex systems can lead to increased risks of error, he explains the potential advantages of using advanced techniques to inform trading decisions. Ultimately, the article serves as an exploration of how adding machine learning layers can enhance traditional trading methods.
The Value of Blogging in Trading
Carlos reflects on the unexpected opportunities that blogging has created for him in the trading community. He emphasizes the connections he has formed with diverse individuals, ranging from retail traders to hedge fund managers, highlighting the importance of sharing knowledge and experiences. Publishing his thoughts and strategies not only showcased his expertise but also revealed a significant demand for profitable trading strategies and educational resources. His blogging experience underscores the potential for personal growth and collaboration within the trading landscape.
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