Marc Filias

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Applying the Wilcoxon Signed-Rank Test to Evaluate Bitcoin Trading Strategies

In the fast-paced and volatile world of cryptocurrency trading, reliable statistical methods for evaluating trading strategies are essential. The Wilcoxon Signed-Rank Test offers a powerful non-parametric approach particularly well-suited to cryptocurrency markets, where price distributions often violate the normality assumptions required by traditional statistical tests. This article explores how quantitative analysts and crypto traders can […]

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The Shapiro-Wilk Test

The Shapiro-Wilk test is a statistical method used to determine whether a dataset follows a normal distribution. Developed by Samuel Sanford Shapiro and Martin Wilk in 1965, this test is widely employed in various fields of research to assess the normality of data, which is a crucial assumption for many statistical analyses Theory Behind the

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Weekly Reading

February 24, 2025 Breakthrough in Brain-Computer Interfaces: Two-Way Adaptive Technology Revolutionizes Control and Applications: The world’s first two-way adaptive brain-computer interface, developed by researchers from Tianjin University and Tsinghua University, utilizes a memristor-based dual-loop mechanism to achieve a 100-fold increase in efficiency and enhance control capabilities by allowing four degrees of freedom using only brain

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The Kolmogorov-Smirnov Test

The Kolmogorov-Smirnov test is a nonparametric statistical test used to determine whether a dataset comes from a known distribution or if two datasets come from the same distribution. It is named after Andrey Kolmogorov and Nikolai Smirnov, who developed the test. This article will explore the basics of the Kolmogorov-Smirnov test, its applications, and provide

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The Kruskal-Wallis Test

Introduction to the Kruskal-Wallis Test The Kruskal-Wallis test is a non-parametric statistical test used to compare three or more independent groups to determine if there are statistically significant differences between them. It is often considered the non-parametric equivalent of the one-way analysis of variance (ANOVA) and is particularly useful when the assumptions of ANOVA, such

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Two-Way ANOVA Example: Sector and Market Cap Effects on Stock Returns

Two-way ANOVA in a financial context Let’s demonstrate a two-way ANOVA in a financial context by examining how both sector and market capitalization influence stock returns: # Set seed for reproducibility np.random.seed(456) # Create a dataset with two factors: market sector and company size sectors = [‘Technology’, ‘Healthcare’, ‘Financial’] company_sizes = [‘Small Cap’, ‘Large Cap’]

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ANOVA (Analysis of Variance): A Comprehensive Guide with Python Implementation

Introduction Analysis of Variance, commonly known as ANOVA, is a statistical method used to test differences between two or more means. It was developed by statistician Ronald Fisher in the 1920s and has become a fundamental technique in experimental research across various fields including psychology, biology, medicine, and social sciences. ANOVA helps researchers answer a

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Weekly Reading

November 11, 2024 OpenAI’s Strategic Domain Acquisition and AI Strategy: OpenAI’s acquisition of Chat.com highlights its commitment to enhancing brand visibility in the competitive AI landscape. This move complements its rebranding efforts, including the introduction of a new model naming convention with the ‘o1’ series. By securing high-value domains, OpenAI aims to simplify user access

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