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Continue reading →: Analyzing Financial Trends: Kalman Filtering for Gold vs BitcoinIn their paper “A Synchronized Multi‑IMU Wearable System for Tracking of Joint‑Angles in Sports Motion Analysis” (arXiv:2607.26027v1), Samarasekera and colleagues set out to solve a very practical problem: how to reliably measure joint angles in dynamic sports movements using wearable IMUs. Their goal was to design a synchronized pipeline that…
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Continue reading →: Understanding Tail Analysis in Financial MarketsIn financial markets, distinguishing between information-driven movements and liquidity-driven shocks is critical. The reference study we based our work on highlights the importance of tail analysis: comparing Gaussian (thin-tailed) and Student‑t (fat-tailed) distributions to understand whether price changes are more likely to reflect genuine information or temporary liquidity imbalances. Financial…
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Continue reading →: Auditing LLM Trading: Bridging Theory and Market Reality with the GT table in RIntroduction: The Laboratorial Illusion In quantitative finance, Large Language Model (LLM) multi-agent systems are frequently celebrated for their theoretical intelligence. Financial data scientists spend months refining prompt semantics, building complex reasoning frameworks, and engineering multi-turn debate loops between specialized agent nodes. On paper—and within simulated environments—these networks demonstrate flawless predictive…
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Continue reading →: A Multi-Agent DDQN Strategic Audit Engine for Silver Markets using Keras/TensorFlow1. Introduction & Theoretical Framework In modern electronic trading markets, algorithmic execution engines drive the vast majority of institutional order flows. Evaluating whether these independent, learning-driven trading algorithms behave competitively or tacitly coordinate has become a critical challenge for quantitative compliance, market microstructure design, and risk management. This technical article…
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I’m Selcuk Disci
The DataGeeek focuses on machine learning, deep learning, and Generative AI in data science using financial data for educational and informational purposes.
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