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.
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.
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.
Mercury Bot's market analysis system is evolving beyond traditional candlestick patterns to focus on pure indicator-based setups. This guide introduces 42 "sweet spots" - specific market conditions that can trigger high-probability trading signals. Each setup is defined by a combination of technical indicators and specific market conditions.
Mercury Bot's market analysis system has evolved to include a sophisticated set of time-based trading conditions. Each condition is designed with a specific timeframe target, making them ideal for automated trading systems. This guide introduces 42 "tasty" market conditions that combine technical indicators with precise time-based profit targets.
Below is a curated set of 42 "tasty market condition" names—similar in style to SWING_BUY_MARKET_TP_IN_3D—each with a short definition and a brief usage tip. They're meant to be catchy shorthand for specific trade setups that your system can detect and exploit.
Picture this: A trader sits in front of multiple screens, each displaying different market indicators, trying to make sense of the crypto market's chaotic dance. The screens flicker with RSI waves, MACD crossovers, and Bollinger Band squeezes. But something's missing – a way to bring all this data together into clear, actionable insights.
In the neon-drenched underbelly of Silicon Valley, where bits dance like digital fireflies and repositories lurk in the shadows of the cloud, there lived a legendary figure known only as the Terminal Whisperer. They say she could speak to the machines in their native tongue, commanding git flows with mere whispers of commands.
Setting up a production-ready Kubernetes cluster can be a challenging journey, filled with unexpected twists and turns. Today, we want to share our experience of deploying the Mercury Bot on Kubernetes, complete with proper networking, HTTPS, and all the fun debugging that came with it.
In the realm of cloud computing, where digital titans roam, there exists a tale as old as distributed systems themselves. This is the story of Kubernetes, the container orchestrator, as it might have been told in ancient Greece.