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Backtest statistics are essential for evaluating the efficacy of investment strategies.

Overfitting happens when a strategy is tailored too closely to historical data, making it unlikely to perform well on new, unseen data.

This blog post focuses on a novel way to backtest trading strategies, aiming to reduce the chances of backtest overfitting.

In trading, bet sizing plays a crucial role. Inconsistent or improperly sized bets can lead to losses even when predictions are accurate. This blog discusses various strategies to optimize bet sizes.

Investment strategies usually involve making bets with an exit plan based on two conditions - achieving a profit or stopping losses. The interaction between these factors is best understood as a binomial process that evaluates the strategy's resilience against minor changes.

Correlation measures only linear codependence, which can be misleading.

Overfitting can be especially problematic when we rely solely on backtesting for validation. To improve model performance and interpretation, one must look beyond simple backtesting and consider other analyses like feature importance.

Structural breaks in financial markets refer to a shift from one type of market behavior to another, like from a mean-reverting to a momentum pattern.

This blog post focuses on a novel way to decompose to signal and noise.

Many people use Time bars to make sense of financial market data, but they're not the best choice. Why?

Differentiation to an integer degree is used to make a series stationary. However, fractional differentiation allows the exponent to be a real number. This helps to preserve memory.

To train a machine learning model, we usually need a labeled dataset. In the world of finance, this involves creating a matrix of features, $X$, and an array of labels or values, $y$. In this blog, we'll delve into various methods of labeling financial data.

You might have noticed that many financial models rely on the assumption that data points are independent and identically distributed (IID). However, this is often not the case in real-world financial applications.

In traditional settings, cross-validation is an effective tool for evaluating a machine learning model's performance. However, the complexities of financial data pose unique challenges.

When dealing with a set of training observations and outcomes, you would want to estimate a function that closely matches the true function.

Hyper-parameter tuning is crucial for optimizing machine learning (ML) algorithms. Effective tuning results in improved real-world performance.

A cutting-edge method for constructing financial portfolios, called Hierarchical Risk Parity (HRP). This method overcomes challenges posed by traditional techniques, by using modern mathematics and machine learning.