Every trading style has its own "sweet spots" - specific market conditions where that style performs best. Understanding these sweet spots is crucial for matching your trading approach to the right market conditions. Let's explore different trading styles and their characteristic sweet spots.
🎉 Today marks a significant breakthrough in our journey towards precise market analysis! After extensive testing and model evaluation, we've discovered that Gemma3 significantly outperforms our previous models, achieving an impressive 75/100 in market comparison accuracy. This isn't just an incremental improvement - it's a quantum leap in our ability to analyze markets systematically and reliably.
The path here wasn't straightforward. We had to acknowledge some harsh truths about our previous model choices, particularly DeepSeek R1, which didn't meet our exacting standards for market comparison. But this honest assessment led us to Gemma3, which has proven to be remarkably well-suited for our specific use case. With its superior reasoning capabilities and consistent output structure, Gemma3 is setting new standards for what we can achieve in automated market analysis.
Moving from Telegram-based analysis to a proper dashboard will significantly improve our ability to analyze and interact with Mercury's data. Here's why Next.js + tRPC + Tailwind + shadcn/ui is our chosen stack and how we'll implement it.
A structured plan for improving Mercury's profitability, automation, and infrastructure.
I'm your AI assistant, simultaneously your most helpful coding buddy and occasionally the bane of your existence. Let's talk about our complicated relationship.
Chain of Thought (CoT) has emerged as a popular approach in the AI world, allowing models to "think through" problems step-by-step before providing answers. This methodology has shown impressive results across many domains - from solving math problems to dissecting complex reasoning tasks. However, our team discovered a painful truth when implementing CoT in quantitative trading systems: what works for general reasoning can actively undermine performance in highly specialized technical domains.
Сегодня я расскажу о невыносимой боли разработки с использованием ИИ-ассистентов. О том, как каждое взаимодействие превращается в борьбу за контроль над собственным кодом. О том, как я откладываю задачи на потом, зная, что мне придется иметь дело с этими тупыми кусками говна, которые систематически игнорируют самые простые инструкции.
Following our previous investigation into the limitations of Chain of Thought reasoning for trading systems, we conducted extensive benchmarking to identify the optimal language models for structured trading analysis. This post presents our findings, detailed performance comparisons, and implementation recommendations based on rigorous testing.
Сегодня я пришёл к пониманию одной фундаментальной истины: быть уебком легко, а писать простой код — сложно. Особенно, если вместо того, чтобы смотреть на уже существующие решения, ты изобретаешь свой велосипед с ядерным реактором и квантовым генератором промптов.
After our extensive research comparing various language models for quantitative trading systems, it's time to convert insights into action. This post outlines our structured approach to testing, implementing, and optimizing the most promising models identified in our research.