• Aug 11, 2025 Algorithmic Trading Ernest Chan While Ernest Chan advocates for systematic approaches, he also warns about common pitfalls: - Overfitting: Crafting strategies that only perform well on historical data. - Data 4 Mining Bias: Finding patterns that are purely coincidental. - By Karson Little
• Feb 6, 2026 Machine Learning For Algorithmic Trading olicies through trial and error. - The model learns to maximize cumulative rewards (profits) by interacting with the market environment. Types of Data Utilized in ML-Based Trading The effectiveness of machine learning models hinges on the quali By Zetta Hickle-Heathcote
• Jan 23, 2026 Statistically Sound Machine Learning For Algorithmic Trading allenges, and best practices for applying statistically sound Statistically Sound Machine Learning For Algorithmic Trading 6 machine learning in the context of algorithmic trading. --- Understanding the Foundations of Machine Learning in Trading Before diving into advanced techniques, it’s cruci By Preston Rippin
• Mar 21, 2026 Algorithmic Trading And Quantitative Strategies Translating model outputs into trading signals, indicating when to buy or sell. - Risk Management: Incorporating measures like value-at-risk (VaR), drawdown controls, and position sizing to mitigate losses. - Performanc By Stevie Bogan
