24 posts tagged with "technical-analysis"

Looking to enhance your market analysis without the complexity of LoRA fine-tuning? This deep dive reveals four powerful techniques to supercharge DeepSeek-R1's technical analysis capabilities using just prompt engineering and Ollama's built-in features. Whether you're a quant developer seeking better market insights or an ML engineer optimizing model performance, these battle-tested approaches will help you achieve professional-grade analysis without the computational overhead of custom training.

Looking to move beyond rigid take-profit and stop-loss levels? This comprehensive guide reveals five battle-tested approaches to dynamic TP/SL calculation using technical analysis. Whether you're a systematic trader seeking to optimize risk management or a quant developer building automated systems, these strategies will help you adapt to changing market conditions while maintaining robust position management. From volatility-based adjustments to multi-timeframe confluence, discover how to leverage our technical analysis service for smarter trade exits.

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.

The Problem​

Technical analysis has long relied on combining multiple indicators to generate trading signals. The challenge lies not in which indicators to use - these are well established - but in how to weight their importance dynamically as market conditions change. Traditional fixed-weight systems often fail when market regimes shift, leading to:

• False signals during regime transitions
• Delayed adaptation to new market conditions
• Over-optimization for specific market states
• Poor performance in unexpected market conditions

In our recent work on the Mercury TA service, we encountered an interesting challenge with our Ichimoku Cloud indicator implementation. The issue arose from using primitive boolean values to represent complex market states, leading to ambiguous and sometimes incorrect interpretations. This post outlines our three-step solution to create a more robust and maintainable technical analysis API.

We're excited to announce the release of Mercury TA v1, a significant milestone in our journey to provide robust technical analysis capabilities for cryptocurrency trading. This release represents a comprehensive overhaul of our technical analysis infrastructure, focusing on reliability, performance, and extensibility.

Below are three improved prompt formulations. Each clarifies the trading criteria (e.g., RSI thresholds, MACD crossing specifics, ADX ranges), comparison logic (e.g., how to pick a "winner"), and exact JSON response format, so that different AI models have less room for misinterpretation or data mix-ups.

Introduction​

Mercury Bot's market analysis system is built on a foundation of 42 precisely defined market conditions. Each condition represents a specific technical setup that can trigger trading signals. This guide details how these conditions are implemented and how they drive the bot's decision-making process.

Introduction​

As Mercury Bot continues to evolve, we've identified key technical indicators that could enhance its market analysis capabilities. While we've already implemented several core indicators (RSI, MACD, Bollinger Bands, Ichimoku Cloud, OBV), this guide ranks additional indicators by their value for crypto markets and implementation complexity.