After outlining our BS detection principles and criteria, let's identify the opportunities that are right in front of us. These are validation approaches we can implement quickly using our existing infrastructure.
Following our observations about LLM behavior in technical analysis, a critical question emerges: How do we systematically detect when an LLM is confidently wrong or producing nonsensical analysis? This post explores the tools and approaches available for this challenge.
While implementing large language models for technical analysis and market reasoning, we've discovered several fascinating insights about how these models process and reason about market data. These findings challenge common assumptions about temperature settings and prompt engineering, while offering practical solutions for more reliable analysis.
After losing my job partly due to communication issues (despite being a competent developer), I've learned a crucial lesson: it's not just about what you say, but how you say it. Here's the simple habit I wish I had developed earlier.
ATLAS (AI Trading Logic and Analysis System) represents a new approach to algorithmic trading by combining traditional technical analysis with modern AI capabilities. Unlike conventional trading systems that rely solely on predefined rules or indicators, ATLAS leverages large language models to provide contextual, adaptive trading strategies while maintaining the rigor of systematic trading.
Below is a minimalistic system design for a trading engine that leverages two AI models (“cheap” and “expensive”) within a positive feedback loop. This system stores decision history, orders, and trade data, and can iteratively learn from its own performance. The design is intentionally simple and modular.
[City, Date] – Today marks the launch of Arcana, an innovative Telegram bot that revolutionizes the world of Tarot readings by integrating artificial intelligence and blockchain technology. Arcana offers users personalized and insightful Tarot experiences, all while ensuring transparency and security through the TON blockchain.