KKunkuner

KKunkuner / openclaw-retail-trader

Public

用散户第一视角生成交易想法的skill。Inspired by colleague-skill（同事skill）and ex-skill (前任skill)

31
0
100% credibility
Found Apr 12, 2026 at 31 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

A skill pack for an AI chat tool that simulates the impulsive, emotion-driven decision-making of a Chinese A-share retail trader in first-person style for entertainment and self-reflection.

How It Works

1
📰 Discover the fun trader simulator

You hear about a playful AI tool that thinks and talks like an everyday stock trader chasing quick wins in Chinese markets.

2
📥 Grab the personality pack

Download the simple set of files that bring this impulsive trader mindset to life.

3
🔗 Add it to your AI chat buddy

Tuck the files into your AI helper's special spot for new personalities, and it's ready to go.

4
💬 Start chatting about stocks

Tell your AI to analyze a stock ticker using the retail trader's brain, like 'think like a trader on 000xxx'.

5
🧠 Hear the trader's inner voice

Get exciting first-person thoughts: what type of trader you are, what's swirling in your head, and exactly what trade you'd make.

😄 Laugh and learn from the realism

Enjoy spotting your own trading habits in the emotional rants, gaining insights without risking real money.

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AI-Generated Review

What is openclaw-retail-trader?

This OpenClaw skill lets you generate trading ideas for Chinese A-shares straight from a retail trader's impulsive first-person view, outputting thoughts like "what I'm thinking" and "what I'd do" instead of dry analysis. It simulates emotional biases—FOMO chases, quick profits, slow stops—using OpenAI agents and eastmoney data for real-time prices, volume, and board states. Drop it into your OpenClaw setup, query like "analyze 000xxx as a retail trader," and get persona-driven suggestions on personas like momentum chasers or dip buyers.

Why is it gaining traction?

Unlike rational stock analyzers, openclaw-retail-trader dives into the retail trader's head, prioritizing gut feelings over logic, much like ex-skill or colleague-skill but tuned for A-shares. Developers hook on the vivid, scenario-based outputs that mirror real trader chaos—FOMO rushes, narrative flips—making it a fresh tool for behavioral sims in OpenClaw workflows. Its playbook of psych models and strict first-person rules deliver consistent, entertaining results without setup hassle.

Who should use this?

A-share day traders scripting OpenClaw bots to test retail strategies against emotional pitfalls. Behavioral finance devs prototyping trader personas for apps or education. Quant teams in China wanting quick retail-trader baselines before layering rational overlays.

Verdict

At 31 stars and 1.0% credibility, it's raw and niche—solid README and prompts, but zero tests or broad adoption signals immaturity. Grab it for OpenClaw fun or self-reflection on trading biases, but heed the disclaimer: entertainment only, not for real money.

(178 words)

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