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financial-analysis-agent

Automate financial analysis and investment research using Python libraries.

39|1|Updated Dec 21, 2025
Search Tags:#portfolio management#financial analysis#risk assessment#stock analysis#investment research#trading
One-click install
npx skills add https://github.com/qodex-ai/ai-agent-skills --skill financial-analysis-agent
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: financial-analysis-agent
Source: https://github.com/qodex-ai/ai-agent-skills/tree/main/skills/financial-analysis-agent
Command: npx skills add https://github.com/qodex-ai/ai-agent-skills --skill financial-analysis-agent

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires yfinance, pandas, numpy, scikit-learn, TA-Lib, statsmodels, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill empowers users to perform in-depth financial analysis, conduct investment research, and manage portfolios, transforming raw financial data into actionable insights.

Core Features & Use Cases

  • Data Integration: Connects to financial data sources like yfinance to fetch stock prices and company financials.
  • Analysis Techniques: Implements technical analysis (moving averages, RSI) and fundamental analysis (profitability, valuation, liquidity ratios).
  • Recommendation Generation: Provides data-driven investment recommendations (Buy, Sell, Hold) with confidence levels.
  • Portfolio Management: Enables portfolio value calculation, rebalancing, and risk assessment.
  • Use Case: An investor can use this Skill to analyze a stock's technical and fundamental health, receive a recommendation, and assess its risk before making an investment decision.

Quick Start

Use the financial-analysis-agent skill to analyze the stock ticker AAPL and provide a buy or sell recommendation.

Frequently Asked Questions about financial-analysis-agent

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

FAQPage Schema
How do I perform technical and fundamental stock analysis using Python?▼

Stock analysis in Python is automated by retrieving data via yfinance and applying pandas, numpy, and scikit-learn to calculate technical indicators like moving averages and RSI alongside fundamental profitability and valuation ratios.

Can I generate automated investment recommendations with confidence levels?▼

Automated investment recommendations are generated by analyzing technical and fundamental data to output data-driven Buy, Sell, or Hold signals complete with assigned confidence levels for decision support.

Does this financial analysis approach support portfolio risk assessment and rebalancing?▼

Portfolio risk assessment and rebalancing are supported through analytical techniques that calculate overall portfolio value and evaluate risk exposure using financial data processed with numpy and statsmodels.

What's the best way to retrieve stock prices and company financials for investment research?▼

Stock prices and company financials are retrieved by connecting to financial data sources like yfinance, which feeds raw market data into the analytical pipeline for comprehensive investment research.

Do I need TA-Lib and scikit-learn to calculate moving averages and RSI?▼

TA-Lib and scikit-learn are required dependencies for calculating technical indicators like moving averages and RSI, providing the mathematical and machine learning framework needed for accurate market trend analysis.

How does risk assessment work for individual stocks before making an investment decision?▼

Risk assessment for individual stocks works by evaluating the retrieved financial data and calculated technical indicators to quantify potential exposure, providing a risk profile to inform the final investment decision.

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