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Multi-Agent Stock Analysis

Multi-Agent Stock Analysis

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Multi-Agent Stock Analysis
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What is Multi-Agent Stock Analysis?

Multi-Agent Stock Analysis is an open-source framework that deploys multiple specialized AI agents—DataCollector, SentimentAnalyst, Predictor, and Reporter—to streamline end-to-end stock research. The DataCollector agent fetches real-time prices and financial news. The SentimentAnalyst processes news articles to gauge market sentiment. The Predictor leverages machine learning models to forecast future stock movements. Finally, the Reporter crafts detailed summaries and visualizations. Its modular architecture supports easy customization for different assets, models, and reporting formats.

Who will use Multi-Agent Stock Analysis?

  • Individual investors seeking automated analysis
  • Financial analysts and advisors
  • Quantitative researchers and data scientists
  • Educational institutions teaching AI in finance

How to use the Multi-Agent Stock Analysis?

  • Step1: Clone the repository: git clone https://github.com/shaadclt/Multi-Agent-Stock-Analysis
  • Step2: Install dependencies: pip install -r requirements.txt
  • Step3: Configure API keys in config.yaml for data sources and OpenAI
  • Step4: Customize agent parameters (symbols, date range, models)
  • Step5: Run the main script: python main.py
  • Step6: Review generated reports in the output/reports folder

Platform

  • mac
  • windows
  • linux

Multi-Agent Stock Analysis's Core Features & Benefits

The Core Features

  • Automated multi-agent orchestration for stock analysis
  • Real-time data collection from financial APIs
  • News sentiment analysis using NLP
  • Machine learning–based price forecasting
  • Automated report generation with visualizations
  • Modular, customizable agent definitions

The Benefits

  • Reduces manual stock research effort
  • Integrates diverse data sources seamlessly
  • Offers unbiased sentiment-driven insights
  • Supports extensibility for custom models
  • Enables reproducible, scheduled analysis

Multi-Agent Stock Analysis's Main Use Cases & Applications

  • Daily automated reporting of selected stock portfolios
  • Risk assessment for investment strategies
  • Sentiment-driven trading decision support
  • Teaching AI agent collaboration in quantitative finance

FAQs of Multi-Agent Stock Analysis

Multi-Agent Stock Analysis Company Information

Multi-Agent Stock Analysis Reviews

5/5
Do You Recommend Multi-Agent Stock Analysis? Leave a Comment Below!

Multi-Agent Stock Analysis's Main Competitors and alternatives?

  • QuantConnect
  • FinBERT for financial sentiment analysis
  • TPOT for automated machine learning
  • Custom ChatGPT stock analysis scripts

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