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MarketTrend-LLM-Analyzer

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🚀 Overview

MarketTrend-LLM-Analyzer is a Python-based project designed to scrape financial news articles, extract their full content, and then leverage a local Large Language Model (LLM) via LM Studio for intelligent summarization and market trend analysis. This tool helps quickly get the gist of economic news and understand its potential impact on market sentiment (bullish, bearish, or neutral).

✨ Features

• Article Scraping: Automatically fetches the latest financial articles from boursedirect.fr.
• Full Text Extraction: intelligently extracts the complete article content, even with varied website structures.
• Local LLM Integration: Communicates with a local LLM (e.g., Mistral-7B) running in LM Studio for offline processing.
• Summarization: Generates concise summaries of lengthy articles.
• Trend Analysis: Assigns a numerical score (+2 to -2) indicating the article's market trend sentiment (bullish, bearish, or neutral).
• Debugging Tools: Includes options to save raw HTML for easier debugging of content extraction.

🛠️ Installation

Prerequisites

Before you begin, ensure you have the following installed:

• Python 3.x: This project is built with Python.
• LM Studio: Download and install LM Studio from https://lmstudio.ai/.
• LLM Model: Within LM Studio, download a suitable model. This project is configured for mistral-7b-instruct-v0.2-GGUF, but you can use others as long as they run well in LM Studio.

Project Setup

1. Clone the Repository (once you create it on GitHub):

   git clone https://github.com/YourGitHubUser/MarketTrend-LLM-Analyzer.git
   cd MarketTrend-LLM-Analyzer

2. Create a Virtual Environment (recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows: `venv\Scripts\activate`

3. Install Dependencies:

   pip install requests beautifulsoup4 openai

⚙️ Configuration

LM Studio

Make sure your LM Studio server is running and accessible at http://localhost:1234/v1. The project's query_llm_via_lmstudio function is pre-configured for this endpoint.

🚀 Usage

To run the scraper and analysis, simply execute the main script:

python your_script_name.py # Replace your_script_name.py with the actual name of your Python file

The script will:

1. Scrape the main news page of BourseDirect.
2. For each article found, it will:
   • Attempt to fetch and parse its full content.
   • Send the content to your local LLM via LM Studio.
   • Print the LLM's summary and the extracted trend score.
3. HTML files of scraped articles will be saved in the project directory for debugging (if save_html=True in scrape_and_analyze function call).

Output Example

🔗 https://www.boursedirect.fr/fr/actualites/xxx
📌 Titre de l'article
📝 Résumé court (page d'accueil) : Ceci est un bref résumé de l'article.
📄 Contenu extrait :
Le contenu complet de l'article irait ici...
...
📃 Réponse du LLM :
Résumé : Cet article analyse... [4-5 phrases de résumé]
Tendance : +1
📊 Trend Score (extrait) : 1
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📄 License

This project is open-source and available under the MIT License.

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