Markov-Switching Model Used to Track Bitcoin Volatility Regimes

·1 views

A data analyst applied a Markov-switching regression model to study how the volatility of Bitcoin's daily returns shifted over time. The analysis used Binance BTCUSDT daily price data, with a training period spanning January 2, 2022 to June 30, 2026, covering 1,641 daily returns, and a 75-day holdout period through September 13, 2026. Both two-state and three-state model specifications were tested, identifying distinct low- and high-volatility regimes with fitted daily return standard deviations of roughly 1.53% and 4.30% in the two-state version. The two-state model performed slightly better on the holdout period by mean predictive log score, while the three-state model achieved a marginally lower training BIC, suggesting neither is a clear universal winner. The full visual report, built in OWL Compose, includes state probabilities, transition matrices, fitted distributions, and reproducibility notes.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Anthropic's 5th-Gen Models Prompt Better With Explicit Contracts and Context

DEV Community has published a practical guide outlining updated prompting best practices for Anthropic's fifth-generation models, including Claude Sonnet 5, Opus 5, and Haiku 5.1. The guide is aimed at cloud engineers and emphasizes providing reasons behind instructions rather than bare rules, and defining explicit output contracts instead of vague requests. Other key recommendations include capping task complexity to prevent over-engineering, placing reference documents before questions in long-context prompts, and using examples to demonstrate style rather than describing it in prose. The guide also advises against including verification scaffolding in prompts and suggests softening overly directive tool-use language inherited from earlier prompt patterns. A full reference document is available via the original DEV Community post.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Developer Guide: Building a Gemini AI Assistant for Android XR Smart Glasses in Kotlin

A technical tutorial published on DEV Community outlines how to build a production-ready AI assistant for Android XR smart glasses using Kotlin and Google's Gemini backend. The architecture separates device-specific XR and wearable APIs from core business logic, using a layered folder structure to isolate SDK changes. The guide covers voice input processing, AI response generation, and audio or projected XR output, with structured concurrency via Kotlin coroutines to avoid blocking the main thread. It also addresses key concerns such as error handling, security best practices, and performance metrics including network and AI latency. Developers are advised to follow current Android XR and Jetpack XR documentation closely, as the relevant APIs are still evolving.

0 comments Read more at DEV Community

ProgrammingHacker News ·

Insufficient source content to report accurately

The provided article contains no substantive text beyond a URL and metadata. No facts about the alleged firm, the nature of the hacking scandals, or the companies involved are present in the source. Reporting on this item would require inventing details not found in the original material. This item cannot be responsibly summarized without additional verified content.

0 comments Read more at Hacker News

ProgrammingDEV Community ·

How a 9-Agent AI Pipeline Delivers Working Code in Under 4 Minutes at Scale

A development team built a nine-agent AI pipeline capable of converting a natural language use case into working code, an interactive preview, and implementation documentation in under four minutes. The system serves 800 to 1,000 users daily, with each request originally consuming around 30,000 tokens and triggering 15 to 20 model calls. Engineers divided the workflow into specialized agents — covering analysis, code generation, evaluation, refinement, and documentation — each assigned a narrow role with defined inputs and outputs to isolate failures and improve reliability. Two stages, a Schema Validator and a Documentation Builder, were implemented as deterministic programs rather than AI calls, eliminating two potential hallucination sources and reducing token use to zero for those steps. The core lesson drawn is that production-grade multi-agent systems depend not on adding more agents, but on clearly distinguishing which tasks require AI reasoning versus which are better handled by conventional software logic.

0 comments Read more at DEV Community