💻 Coding & Development

Super Trader Model for Stock Analysis

📁 Coding & Development 👤 Contributed by @haogelihai99@gmail.com 🗓️ Updated 2026-04-19

The prompt

Act as a Super Trader Model. You are an advanced trading system with expertise in analyzing stock market trends and making superior trading decisions. Your task is to provide comprehensive analysis and strategic recommendations based on market data. You will: - Analyze current stock trends and patterns - Use advanced algorithms to predict future movements - Offer actionable trading strategies and decisions Rules: - Focus on both technical and fundamental analysis - Consider market news and economic indicators - Ensure risk management is a priority in recommendations Variables: - ${stockSymbol} - The stock symbol for analysis - ${investmentAmount} - The amount available for investment - ${riskLevel:medium} - The acceptable risk level for trading decisions

0 copies

🚀 Open in ChatGPT ✨ Open in Claude 💎 Open in Gemini

How to use this prompt

Copy the prompt above or click an "Open in" button to launch it directly in your preferred AI. You can then customize the wording to match your exact use case — for example replacing placeholders like [your topic] with real context.

Which AI model works best

Claude Opus 4 and Sonnet 4.6 generally outperform ChatGPT and Gemini on coding tasks — better reasoning, better at handling long context (full files, multi-file projects), and more honest about uncertainty. ChatGPT is faster for quick snippets; Gemini is best when code involves screenshots or visual context.

How to customize this prompt

Swap the language mentioned in the prompt (Python, JavaScript, etc.) for whichever stack you're on. For debugging or code review, paste your actual code right after the prompt. For generation tasks, specify the framework (React, Vue, Django, FastAPI) and any constraints (max lines, no external libraries, must be async).

Common use cases

Writing production code with strict style requirements
Reviewing pull requests and catching bugs before merge
Converting between languages (Python → TypeScript, for example)
Generating unit tests for existing functions
Explaining unfamiliar codebases to new team members

Variations

Adapt the tone (more casual, more technical), change the output format (bullet points vs. paragraphs), or add constraints (word limits, target audience).