The article delves into the complexities of financial machine learning, highlighting a critical challenge faced by algorithmic trading models: label concurrency. In financial time series, labels that overlap in time create dependencies that contradict the Independent and Identically Distributed (IID) assumption of most ML algorithms. This often leads to overfitted models with poor out-of-sample performance. The solution proposed involves sample weighting based on average uniqueness, quantifying the unique information each data point represents and adjusting weights accordingly. This adjustment aims to rectify model training by prioritizing unique data points. The discussion also introduces three methods to handle non-IID data, focusing on bagging techniques and specific measures to enhance model reliability in trading environments.

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The application of machine learning to algorithmic trading presents unique challenges, especially within financial markets. Traditional supervised learning techniques fall short due to the fluid nature of market targets, unlike more defined domains like medicine. In trading, the target is not fixed, leading to methodological hurdles in model prediction. For instance, modeling financial returns or volatility presents diverse difficulties. This ambiguity in defining targets could hinder performance more than data or model weaknesses.

The inconsistency in target definition across various markets highlights the need for adaptable strategies. Performance ceilings in statistical models often result from methodological limitations. By redefining target variables, empirical model adjustments can enhance outcomes. Experimentation reveals that trading success hinges ...

👉 Read | AlgoBook | @mql5dev

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