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Continue reading →: Breaking the Python Barrier: Building a Pure R-Native DeepAR Engine with LibTorchDeep learning for time series forecasting in R has historically faced a major architectural hurdle: Python overhead. Frameworks like modeltime.gluonts provide interface wrappers around AWS GluonTS, but they rely on a complex execution chain passing through reticulate, virtual environments, Python serialization, and MXNet/PyTorch backends. To overcome the performance bottlenecks and…
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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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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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