adding the intelligence agent

README.md

@@ -47,43 +47,41 @@ Retail investors are at a massive disadvantage. They lack the sophisticated tool
 
 
 
-Local Setup & Installation
-Follow these steps to run the project locally.
-Prerequisites:
-Docker & Docker Compose
-Python 3.10+
-Node.js & npm
+## Local Setup & Installation
+ - Follow these steps to run the project locally.
+ - Prerequisites:
+ - Docker & Docker Compose
+ - Python 3.10+
+ - Node.js & npm
 
 
 1. Clone the repository:
-code
-Bash
+```bash
 git clone https://github.com/your-username/quantitative-analysis-platform.git
 cd quantitative-analysis-platform
+```
 
 
 2. Set up environment variables:
+```bash
 Create a .env file in the root of the project by copying the example:
-code
-Bash
 cp .env.example .env
-
+```
 
 3. Build and run the services:
-code
-Bash
+```bash
 docker-compose up --build -d
+```
 
 
 4. Access the applications:
+```bash
 Frontend: http://localhost:5173
 Backend API Docs: http://localhost:8000/docs
-💡 Key Challenges & Learnings
-Asynchronous Workflow: Building a resilient, multi-stage pipeline with Celery required careful state management and error handling to ensure the process could continue even if one of the scraping agents failed.
-Database Session Management: The most challenging bug was ensuring that the SQLAlchemy database sessions were correctly handled within the forked processes of the Celery workers. The final solution involved a "one task, multiple commits" pattern for maximum reliability.
-AI Prompt Engineering: Crafting the perfect prompt for the Gemini Analyst Agent was an iterative process. It involved structuring the input data and giving the LLM a clear "persona" and a required output format (Markdown) to get consistent, high-quality results.
+```
 
+## Key Challenges & Learnings
+ - Asynchronous Workflow: Building a resilient, multi-stage pipeline with Celery required careful state management and error handling to ensure the process could continue even if one of the scraping agents failed.
+ - Database Session Management: The most challenging bug was ensuring that the SQLAlchemy database sessions were correctly handled within the forked processes of the Celery workers. The final solution involved a "one task, multiple commits" pattern for maximum reliability.
+ - AI Prompt Engineering: Crafting the perfect prompt for the Gemini Analyst Agent was an iterative process. It involved structuring the input data and giving the LLM a clear "persona" and a required output format (Markdown) to get consistent, high-quality results.
 
-Fill in the Blanks:
-Take a great screenshot of your final, beautiful dashboard and save it in your project. Update the path in the README.md.
-Create a .env.example file in your root directory. Copy your .env file, but remove your actual secret keys and replace them with placeholders like your_key_here. This is a professional standard.