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.
During a recent code review of our shadow portfolio feature, we discovered a concerning pattern where FDD documentation was artificially inflated by duplicating TypeScript type definitions. This experience provides valuable lessons about what makes FDD documentation effective - and what doesn't.
In Greek mythology, the interplay between Titans, Gods, and primordial deities shaped the very fabric of existence. Today, I want to share how we've embodied these mythological powers in our modern infrastructure, creating a monitoring trinity that's as powerful as its namesakes.
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:
Below is a list of ten complementary strategy‐condition combinations designed to work in tandem within a weighted–signal system. These combos are intended as starting points that, when combined with a self–balancing feedback loop (which adjusts indicator weights over time based on past performance), can offer diversified, risk–adjusted entries and exits. Each combo uses a different mix of technical conditions—often blending trend, momentum, volatility, and volume—to help ensure that the overall system remains robust even in changing market conditions.
Date: 2024-03-19 Tags: #python #fastapi #technicaldebt #overengineering #whyisthissohard Author: Mercury TA Team
Dear FastAPI maintainers,
I've just spent a week in dependency injection hell, fighting with your framework's response handling that somehow manages to break basic HTTP protocol. Let me share my experience with this overengineered nightmare.
You promised:
What we got:
Here’s a structured plan for your TA School (42 lessons), keeping it engaging, practical, and with the ultimate goal of developing advanced trading strategies. It assumes you have basic knowledge and focuses on building and implementing real-world strategies while skipping the boring theory.
Swing trading is the art of capturing significant price moves over days to weeks. Unlike scalping or day trading, which rely on rapid entries and exits, swing traders aim to profit from broader market movements, capitalizing on momentum, trends, and key price levels.
✅ Less screen time than day trading ✅ High reward-to-risk potential ✅ Works well with technical analysis ✅ Avoids the noise of lower timeframes