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mouseqiao85
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stock_analysis_with_api

Analyze stocks with technical indicators, wave theory, sentiment, and machine learning predictions.

1|Updated Feb 1, 2026
Search Tags:#technical-analysis#machine-learning#sentiment-analysis#stock-analysis#investment-advice#wave-theory
One-click install
npx skills add https://github.com/mouseqiao85/AI-Plat --skill stock-analysis-with-api
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: stock_analysis_with_api
Source: https://github.com/mouseqiao85/AI-Plat/tree/main/agent/skills/stock_analysis_with_api
Command: npx skills add https://github.com/mouseqiao85/AI-Plat --skill stock-analysis-with-api

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires tushare, scikit-learn, snownlp, jieba, scipy, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides comprehensive stock analysis, including technical analysis, wave theory, sentiment analysis, and machine learning predictions, enabling users to make informed investment decisions.

Core Features & Use Cases

  • Technical Analysis: Includes K-line patterns, technical indicators, machine learning prediction models, and trading advice.
  • Wave Theory Analysis: Identifies Elliot wave stages, Fibonacci retracement, and Sperandeo 1-2-3/2B strategies.
  • Sentiment Analysis: Provides a comprehensive sentiment score based on news, social media, policy analysis, and market mood.
  • Use Case: Imagine you want to analyze the stock '002536.SZ'. Use this Skill to get a detailed analysis including technical indicators, wave theory, sentiment, and predictions.

Quick Start

Analyze the stock '002536.SZ' using the stock_analysis_with_api skill.

Frequently Asked Questions about stock_analysis_with_api

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I combine technical analysis and machine learning for stock analysis?▼

Stock analysis combines technical indicators like K-line patterns with machine learning prediction models using scikit-learn to generate actionable trading advice and market trend predictions.

Can I use Elliot wave theory and Fibonacci retracement to analyze stock trends?▼

Yes, wave theory analysis identifies Elliot wave stages and calculates Fibonacci retracement levels, also applying Sperandeo 1-2-3 and 2B strategies to evaluate market trends for investment decisions.

How does sentiment analysis work for stock investment advice?▼

Sentiment analysis uses snownlp and jieba to process financial news and social media, generating a comprehensive sentiment score based on policy analysis and current market mood.

Do I need real-time stock data to run machine learning predictions?▼

Yes, machine learning predictions require real-time stock data accessed via tushare to feed scikit-learn models for accurate risk assessment and investment decision-making.

What's the best way to apply Sperandeo 1-2-3 and 2B strategies in technical analysis?▼

The best way is using a comprehensive analysis tool that integrates Sperandeo 1-2-3 and 2B strategies directly with K-line patterns to identify market reversals and trading opportunities.

Why does my stock sentiment analysis require Chinese text processing libraries?▼

Sentiment analysis requires jieba and snownlp to accurately tokenize and analyze Chinese financial news and social media text, which is essential for generating comprehensive market mood scores.

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