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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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Continue reading →: Differential Machine Learning with Twin Networks in R: Forecasting Bitcoin with Volatility ProxiesIntroduction Differential Machine Learning (DML), as introduced in the recent arXiv paper (Differential Machine Learning for 0DTE Options with Stochastic Volatility and Jumps), extends supervised learning by incorporating not only function values but also their derivatives. In financial contexts, this often means sensitivities such as Greeks. However, when direct derivatives…
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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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