yourquant_rick on Instagram: "Cross-validation is the backbone of machine learning validation. But what works in image recognition or NLP breaks down completely in financial ML. Traditional k-fold cross-validation assumes data points are independent, identically distributed, and exchangeable — none of which are true in financial markets. Market data is non-i.i.d.: Prices are autocorrelated (yesterday influences today). Volatility clusters. Regimes shift between bull, bear, and sideways markets.
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yourquant_rick on Instagram: "In most machine learning fields, hyperparameter tuning is about maximizing performance on validation sets. Grid search, random search, or Bayesian optimization are applied until the “best” set of hyperparameters is found. But in financial ML, this approach is dangerous. Financial data is noisy, non-stationary, and limited. Aggressive hyperparameter tuning amplifies overfitting, tailoring the model too closely to historical quirks that won’t repeat. The result: strat
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yourquant_rick on Instagram: "Chapter 6 of Python for Algorithmic Trading marks the transition from handcrafted statistical analysis to machine learning (ML) models that can detect patterns in financial data automatically. It explains how supervised and unsupervised learning methods can be applied to trading problems, while stressing the dangers of overfitting noisy markets. Key topics and recipes include: Supervised learning basics: Linear regression, logistic regression, decision trees, random
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